Leadership Style at Coca-Cola Company – May 4th, 2011 ________________________________________ The Coca-Cola Company (NYSE: KO) is a beverage retailer, manufacturer and marketer of non-alcoholic beverage concentrates and syrups. The company is best known for its flagship product Coca-Cola, invented by pharmacist John Stith Pemberton in 1886. The Coca-Cola formula and brand was bought in 1889 by Asa Candler who incorporated The Coca-Cola Company in 1892. Besides its namesake Coca-Cola beverage, Coca-Cola currently offers more than 500 brands in over 200 countries or territories and serves 1. billion servings each day. [3] The company operates a franchised distribution system dating from 1889 where The Coca-Cola Company only produces syrup concentrate which is then sold to various bottlers throughout the world who hold an exclusive territory. The Coca-Cola Company owns its anchor bottler in North America, Coca-Cola Refreshments. The Coca-Cola Company is headquartered in Atlanta, Georgia. Its stock is listed on the NYSE and is part of DJIA, S&P 500 Index, the Russell 1000 Index and the Russell 1000 Growth Stock Index. Its current chairman and CEO is Muhtar Kent.

In the past year, I’ve had the honor of speaking on a number of university campuses around the world — from the London Business School to IMD to MIT. I always walk away from these experiences energized and inspired by the fresh thinking… the rigorous debate… and the entrepreneurial spirit I find at our world’s leading business schools. Coming to Wharton, however, is truly special. This, of course, is where collegiate business education began. More importantly, this is where many of the leading ideas are coming from that will shape business leadership and business development in the years ahead.

Believe me… new ideas and new thinking have never been in more demand in the business world. It’s tough out there at the moment — very tough. The global financial crisis has touched everyone in the world, and it will probably get tougher before it gets better. Last week, I met with Chairman Bernanke in Washington and we talked about the need to keep the faith in our global markets. I acknowledged the issues were difficult but I also expressed my optimism about the future and our belief that this was exactly the right time to invest in our future.

We both agreed that the American economy and the world economy could come out of this crisis stronger and better prepared for the future than when we entered into it. I am a firm believer that times like these are not an excuse to sit back and ride out the storm. Rather, this is the time to focus on what matters most to our business, shed what is wasteful and unproductive, and invest in our brands, customer-facing communications and execution. History has shown us, time and again, that world-class organizations and leaders proactively manage turbulence to sow the seeds for future growth and prosperity.

With that said, I’d like to spend some time this afternoon talking about leading in challenging times. My hope is that the world you will inherit upon completion of your MBA will have calmed down quite a bit. Rest assured, though, that whether it’s a financial crisis, or a geopolitical crisis, or an energy crisis, or an environmental crisis — turbulence will be the new norm in the years ahead. And that’s not necessarily a bad thing, either. I believe that for every headwind we confront there’s an equally powerful tailwind to be ridden.

The trick is finding it. We are living in a world of great paradox. A world of seemingly insurmountable challenges… but also one of breathtaking opportunities. Your leadership will help shape and define that world. I want to get this leadership discussion started by sharing with you some defining moments in my career and how they shaped my own personal leadership style. Then I’d like to share a few thoughts on how I am putting those principles into practice as we lead The Coca-Cola Company and system through these times of great paradox.

Mostly, though, I’m looking forward to hearing what’s on your mind, and my hope is that we can have a great conversation today as well. So, let me start with my career journey and some of the key lessons I learned along the way. When I first went to work for The Coca-Cola Company in Atlanta 30 years ago, America and the world were in a situation surprisingly similar to today. Fuel prices were spiking. A recession was draining our confidence. Across America there was widespread fear that we were losing our global political and economic leadership around world.

Many people feared that a surging Japan would cripple American industry, jobs and the U. S. economy. Even greater numbers of people were worried about their jobs being replaced by technology. But the system didn’t collapse. In fact, America got stronger… much stronger… and that’s because this great nation did what it has always done best — America innovated and reinvented itself. By the time I was 29, the early ’80s economic boom was beginning to heat up both here in America and around the world. I was appointed to lead our first Pan European Customer Relationship function.

A few years later, I was appointed director of International Accounts for Europe. Both of these roles taught me the importance of relationship-building and leading not by hierarchal control but by influencing and collaborating. Four years later, I was named president of our small and troubled operations in Turkey — that was back in 1985. Our business in Turkey at the time had $55 million dollars in revenue. Today it’s among the most successful operations in our system. Being president at the age of 32 in a crisis situation taught me a lot about compassion… listening… mpathy… and building trust — while leading with confidence. A few years later, as the Berlin Wall was falling, I went on to lead our operations in East Central Europe. These were dynamic times — half a billion people from the Baltic to the Balkans lived behind an Iron Curtain for half a century. Our business had no relevance in this geography at the time. There was no infrastructure and no real currency. We did business through counter-trade. We ramped up quickly and built Western-style production and distribution facilities in record time. In fact, we built 24 factories across 11 countries in 28 months.

Today, we are the undisputed market leaders in these geographies and they are among the most innovative markets in our system. Key lessons here for me were prioritization … acting with urgency… … focusing on execution … and keeping your eye on the prize. In 1998, I became CEO of Efes Beverage Group, a small Turkish beer company that had just invested in the Coca-Cola bottling system. At the time, we were a local business, with less than $300 million in revenue. Our vision was to establish “Beverage Leadership from the Adriatic to China. ” By 2005, Efes had expanded to eight countries, was the No. beer company in Europe, and was among the top 10 bottlers in the Coca-Cola system. Revenues had grown to nearly $3 billion, we were listed on both the Istanbul and New York stock exchanges, and we had indeed become the largest beverage company from the Adriatic to China. The big lessons for me here were… … respect for cash… … governance… … and executing by building emotional networks among people. In May 2005, I came back to work for The Coca-Cola Company, first to run our Asia-Pacific operations in Hong Kong. In 2007, I became president and COO. I assumed the CEO position this past July.

The big lessons of the past two years have been the importance of getting our system aligned and our people believing in winning again. Throughout my 30-year career, I’ve been fortunate to interact with some truly wonderful leaders — leaders from business, government and civil society. Regardless of their background, I’ve noticed one overarching and essential trait found in all leaders — and especially leaders who manage through challenging times. They have the ability to create a clear and compelling vision for their organization, and to inspire their people to achieve that vision.

Let’s face it, vision without execution is merely daydreaming. And execution without vision is like running in the dark — you’re moving but probably not in the right direction. One of my first priorities as CEO has been to guide our entire system toward a system-wide vision for our business… and to develop the capabilities to execute against that vision. It became apparent to me and to my leadership team that the world we inherited at the beginning of 2008 was shaping up to be significantly different than it was even a year ago.

We saw some things happening today and over the near horizon that were going to fundamentally and dramatically reshape the nature of our business over the next decade and beyond. Changes that were going to make the last 10 years seem downright tranquil. Specifically we saw — and continue to see — four massive global trends unfolding that will have great bearing on the world and our system in the years ahead. These are: 1. The rising demand (and cost) for energy. 2. Rising food prices. 3. A growing middle class and… 4. Rapid urbanization.

Collectively — I refer to them as the “New Equilibrium” because they are shifting the balance of the world and will so for next decade and beyond. The current global financial crisis — while painful and pervasive — did not make this list because I believe it will be a distant memory in a few short years. As traumatic as it seems today, it will have little material impact on the much broader global developments already in place. Let me just take a moment to review these four key developments in more detail because they are really the triggers for the strategic renewal process that we are going through right now.

First, the energy situation. Despite recent fluctuations, most energy experts today believe that oil demand and prices will rise significantly over the next decade as consumption continues to surge. This is already fueling one of the largest transfers of wealth in history. The United States will transfer nearly $500 billion dollars to oil producing economies this year alone. This will join the $2 trillion dollars that will be transferred out of the EU, China, Japan and India this year. Of course, oil booms and busts are not exactly uncommon.

What is different today, however, is the surging energy demand we’re seeing from fast-growing economies around the world, adding to the already huge demand from the developed world. The second component of this New Equilibrium is producing some unintended and far-reaching consequences. The surge in production of bio-fuels like ethanol, along with erratic weather, is partly responsible for food shortages and rising prices. So, here we are with both higher energy costs… and higher food costs.

As populations continue to expand and living standards continue to rise across the world, we will have to manage our business in an economy of constant scarcity and cost pressure. This is the new normal. The third major shift we’re seeing is the rapidly growing middle class. Think about this: A billion new people worldwide will enter the middle class by the year 2020. These new middle class consumers will strive for the same things we want out of life — better quality food and better quality beverages. And like their counterparts in the developed world, most of these new middle class consumers will reside in urban areas.

This brings us to the fourth component of this New Equilibrium — the stunning rate of urbanization taking place around the world. Within the next 12 years, China, India and the Southern Hemisphere will be more urban than North America, Europe and Japan. So, clearly, what you have here with these four global shifts are significant challenges and opportunities that impact the sustainability of our industry… our business… and our planet. For our industry, this also means more people… with more wealth… leading highly mobile, on-the-go, urban lifestyles that are conducive to greater demand for ready-to-drink beverages.

It also means increasing cost pressures, environmental pressures, commodity scarcity, NGO pressures, global talent competition and a host of other implications. Amid this backdrop of opportunity and turbulence, we truly believe that there is no better consumer products industry to be in than non-alcoholic ready-to-drink beverages. At Coca-Cola, we think we’ve really just scratched the surface of our potential. Consider this: This year, people around the world will consume 1. 5 billion servings of our products every day… but 50 billion servings of ALL other beverages.

There is an incredible opportunity for growth. Ours is a $650 billion dollar industry that is growing faster than the world’s GDP and faster than all other consumer packaged goods, including cosmetics, alcoholic beverages, and household care. By 2020, the non-alcoholic beverage space will be a trillion dollar industry. That’s a huge opportunity. If our industry were a nation, we would be among the top 18 largest economies in the world today. We know that capturing new opportunities, however, is going to require both vision and execution across our Company and our wonderful system of bottling partners.

And that’s where our vision — which we call Vision 2020 — comes into play. It’s a look at where our Company and our bottling partners need to be heading over the next decade. Our vision is centered on capturing the unprecedented opportunities emerging over the next decade in the global non-alcoholic beverage industry… … as a billion new people enter the middle class… …as expanding youth markets and affluent aging populations seek new beverage experiences and requirements… … and as breakthrough technologies and innovations reinvent consumer marketing and shopper experiences.

In short, our vision is to harness new wealth, new beverage requirements and new innovations to accelerate growth and create the world’s most respected consumer goods system. I’ve been spending a lot of time this year getting my leadership team assembled and aligned in a way so that I am confident we can execute against our vision and strategies. I think it’s Professor Useem here at Wharton who refers to “leadership as a team sport. ” He couldn’t be more right. Getting the right team in place to execute our vision and strategies has been absolutely critical.

Equally important is getting our bottling system leadership and our Company employees aligned behind our vision. About two months ago, we assembled our top global bottlers and Company leadership for a rigorous two days of discussion on our vision. This was the first time we had convened as a system since the Roberto Goizueta era. We spent a lot of time looking at our business and where we wanted to take it, and we also listened to some leading outside thinkers give their perspectives on the future. We heard from Fareed Zakaria, the noted international affairs experts, former U.

S. Secretary of State Madeleine Albright, the CEO of Goldman Sachs, Lloyd Blankfein (who joined us just days after the start of the global financial meltdown), and Dr. Rajendra Pachauri, the Nobel Prize winning climate-change scientist. Most importantly, we received great input from the leaders of our bottling system about what we need to be doing better to grow as a system. It was a rich discussion and we followed that with another important gathering just two weeks ago when we brought together our employees for a town hall to discuss our vision in Atlanta.

Getting our system leadership and people behind the vision and giving them an opportunity to contribute to the vision will play an essential role in the success of this renewal process. Other extremely important constituencies that we need to communicate vision to are shareowners, retail customers, suppliers, government officials, NGOs and other parties who directly and indirectly influence our ability to grow. There are two critical things a CEO cannot delegate and will have to own. 1. The first is communicating vision… and I’ve already talked a bit about that. . The second is owning the development of leadership talent and succession planning. If you look at my senior management team, you’ll see that we’ve got leaders from Mexico, Lebanon, the UK, Australia, Liberia, Turkey, France, Colombia, Ireland and the U. S. At times, Coca-Cola resembles the United Nations… and in fact we are in more markets than our represented by the UN today. This extraordinary diversity of ideas and cultures and beliefs is undeniably one of the most important competitive advantages we have as a business system.

I sense more and more businesses will begin to resemble us in this regard in the coming years. In fact, CEOs in the US and Europe recently told an Economist survey that their senior management teams will become more international over the next three years. We feel strongly that the next generation of leadership will need to be able to recognize and harness the power of diversity. One of the most fulfilling diversity programs I am personally involved in is serving as the chair of our Company’s Women’s Leadership Council.

In this role, I work with senior women executives throughout our company to identify strategies to attract and develop more women into leadership positions. The keen insights women bring to our business are profound, to say the least. Today, women account for the majority of purchase decision makers for our beverages. Globally, women make up 70 percent of all grocery shoppers. As more and more women around the world gain economic power, we need to be there with the right shopper insights, the right mix of products, and the right marketing and merchandising strategies.

Women’s leadership has never been more important. As the CEO, my job is to create a climate of success for our people and inspire them to achieve the vision we have created for our business. That’s really the true essence of leadership. At the end of the day, it all comes down to execution. As I mentioned earlier, vision without execution is simply daydreaming. For The Coca-Cola Company, execution involves focusing on our three core capabilities of… consumer marketing — which generates that bond and emotional connection with our consumers… ommercial leadership — which involves all the strategic actions we take with our 20 million retail customers who sell our brands around the world each day… and franchise leadership — which is working with our 300 bottling partners around the world to create greater system alignment. Fore effective leadership — especially in these times of challenge, there can be no substitute for strategic thinking and tireless, relentless execution. There can be no alternative for attracting and retaining the absolute best people to lead and creating a dynamic environment for them.

And there can be no job more important than communicating effectively with your customers and all your key stakeholders. As you can see, our strategic renewal is a journey that’s at its beginning stages. But by formulating a clear and compelling vision… getting our system aligned behind it… executing… and constantly communicating our intentions — I’m extremely confident that we will succeed. Muhtar Kent is Chairman of the Board and Chief Executive Officer of The Coca-Cola Company. Mr. Kent joined The Coca-Cola Company in Atlanta in 1978 and has held a variety of marketing and operations roles throughout his career.

In 1985, he was appointed General Manager of Coca-Cola Turkey and Central Asia. From 1989 to 1995, he served as President of the Company’s East Central Europe Division and Senior Vice President of Coca-Cola International, with responsibility for 23 countries. Between 1995 and 1998, Mr. Kent served as Managing Director of Coca-Cola Amatil-Europe, covering bottling operations in 12 countries. From 1999 until his return to The Coca-Cola Company in May 2005, he served as President and CEO of the Efes Beverage Group, the majority shareholder of Turkish bottler Coca-Cola Icecek.

Headquartered in Istanbul and listed on the London and Istanbul Stock Exchanges, Efes is a publicly traded beverage enterprise whose Coca-Cola and beer operations extend from the Adriatic to the Pacific Ocean. Under Mr. Kent’s leadership, Efes experienced extraordinary growth, with triple-digit revenue growth and a 250 percent increase in market capitalization. During that time, in addition to taking Efes Breweries International public on the London Stock Exchange, Mr. Kent also served as a board member of Coca-Cola Icecek. Mr.

Kent was named President and Chief Operating Officer of The Coca-Cola Company’s North Asia, Eurasia and Middle East Group from 2005 until early 2006, where he was responsible for the operations across a broad and diverse geographic region that included China, Japan and Russia. Mr. Kent served as President of Coca-Cola International through most of 2006, responsible for operations outside of North America, until his appointment as President and Chief Operating Officer of The Coca-Cola Company, overseeing all operations of the business, including Bottling Investments.

He succeeded Neville Isdell as Chief Executive Officer of the Company on July 1, 2008, and as Chairman of the Board of Directors on April 23, 2009. Mr. Kent holds a bachelor of science degree in economics from Hull University, England, and a master of science degree in administrative sciences from London City University The news last week that The Coca-Cola Company’s heir apparent and chief operating officer, Muhtar Kent, is to succeed E. Neville Isdell as chief executive officer in July next year will have come as a huge relief to those with a vested interest in the soft drinks giant’s success.

Kent has been groomed by Isdell for the position since he rejoined Coke in May 2005 and became president in December 2006. However, Coke’s recent track record in executive succession made nothing a certainty, and a repeat of the last two CEO appointments, which left the company demoralised and haemorrhaging executive talent, could never be ruled out. Most recently, this was seen four years ago when Isdell came out of retirement to beat then COO Steve Heyer to the CEO’s chair. For some, Heyer was Coke’s natural heir and the decision to pass him over left the boardroom and management divided.

Although Isdell’s track record over the last four years suggests the move was the right one, at the time it summed up a company that was struggling to convince itself or its investors that it had any direction at all. Isdell, who will remain as chairman until the company’s annual shareholders’ meeting in April 2009, has worked hard to turn that situation around. Those looking back over his tenure will judge positively a man who has overseen rejuvenated beverage sales and profits across the world, improved bottler relations and the arrival of new talent to replace that which had been lost during a difficult time in the company’s history.

But perhaps, given the past, his greatest triumph has been to ensure a smooth handover to Kent, as this epitomises the new confidence the Atlanta-based group wants to portray. “I have a very, very strong belief that successful management transition is one of the key jobs of the CEO and the chairman,” Isdell said in an interview recently. “It’s one of the key goals I set for myself. … I’ve been working on it for three and a half years, and I’m trying to do it the right way. ” That “right way” was to convince Kent to rejoin the Coke family after a stint as president and CEO of the Turkish drinks company Efes Beverage Group.

Before that, he had been in the Coke ranks since 1978 in a variety of marketing and operating roles. But his relationship with Isdell really grew from 1989 to 1995, when he was president of Coke’s East Central European division. During this period, Isdell and Kent jointly oversaw the company’s successful move into Eastern Bloc countries as the Iron Curtain fell. His rise up the Coke ranks was not without its hurdles though, and after three years at the helm of Coca-Cola Amatil Europe, he left the company in 1998, following controversy surrounding insider-trading allegations.

Kent settled out of court and continues to deny any wrongdoing. On balance, however, the drinks community seems to think Coke has got its man. “We have confidence in the board’s judgment and continue to recommend KO [Coca-Cola] shares believing that operating performance will continue at or above long-term targets,” said Stifel Nicolaus beverage analyst Mark Swartzberg. Importantly, Kent has been right at the forefront of so much of the progress that has been made at Coke in recent years.

Isdell has turned to him to fix troubled international markets and he headed up the company’s largest acquisition, its US$4. 1bn purchase of Energy Brands, the producer of Glaceau Vitaminwater. His stock is also high amongst the bottlers after working to repair relations with some of the bigger partners. John F. Brock, president and CEO of Coca-Cola Enterprises (CCE), said last week: “Muhtar and Neville are a strong leadership team. Muhtar has a solid understanding of our unique challenges and opportunities in North America and Europe. “

Kent still faces challenges. North America remains problematic for carbonated drinks and Coca-Cola is still playing catch-up with rival PepsiCo in efforts to diversify a portfolio still heavily reliant on this unspectacular category. Improving morale in-house is also a job in progress – the self-inflicted wounds of the beginning of the decade are deep. But in truth the only black mark appears to be the decade-old insider-trading fiasco at Amatil. “This event may be taken as an occasion to re-raise the question of Mr Kent’s ethics,” warned Swartzberg.

Coca-Cola’s board had an outside law firm investigate the matter before re-hiring Kent and as Swartzberg points out: “Local regulators ultimately dropped their investigation of the matter and…this subject has been vetted by Mr Isdell and the board, implying the board’s knowledge of and confidence in Mr Kent’s character is high. ” However, others believe the matter still needs addressing. “The issue remains on investors’ minds and, given Kent’s increasing role at the company, we think Coke will need to continue trying to get investors comfortable with the situation,” another analyst was quoted saying.

At the moment, Isdell talks as if the issue is closed, but many in the investment community still seem to feel there are questions to answer. Convincing them otherwise could be Isdell’s final triumph. here are four main types of management styles that each business would use. Coca Cola have four principles of citizenship that they would have to incorporate into the management style: * Provide quality in the marketplace * Enrich the workplace * Preserve the environment * Strengthen the community A management style is an overall method of leadership used by the manager.

The Coca Cola Company use the following management styles, but each one in different departments. There are three main types of management styles used in businesses: Autocratic Where the leader makes all the decisions, there is no negotiation and is very prescriptive and there is little job satisfaction. However, the job gets done quickly and there is less conflict between different ideas. This style is hardly used among the company as they believe that the lack of input could lead to poor results.

Autocratic does save a lot of time as quick decisions can be made and there is no time wasted on discussion resulting in the business saving time and money. Democratic This emphasises on group agreements to generate new ideas. There are two types of democratic management styles; democratic and consultative democratic. Democratic is where all the managers, junior managers and employees are involved in the ideas and final decision process. Out of all the workers, no-one has a higher level than the others n this management style. Consultative democratic

This is where the managers allow the employees to make the ideas but the ideas are forwarded to the executive’s or the manager consults their team to make the final decision. Coca Cola applies consultative management style to the company more as there can be less conflict for what the final decision is. The advantage of this is that it helps to motivate staff as they are aware that they have a say in the company to some extent. The disadvantages of this that the process is very time consuming and effort will be needed by a manager to do this. Management encourages employees to set goals in line within the organization aims.

There are reviewed regularly in performance appraisals. The advantages of this style are that it will increase efficiency of individuals and help to motivate them and train them so they are productive. The disadvantages of this are that it needs to be well organized and will not work in highly structured jobs. Democratic style is the management style that Coca cola adopts. This sort of management style involves empowerment. In this management style individuals and teams are given responsibilities and decisions to make, usually within a given framework.

If anything wrong happens then the individuals and teams are then held responsible for the decisions that are chosen. With this type of management style it allows the manager to feel comfortable with other people in the organization making some of the decisions. Democratic managers will often want feed back from their employees on decisions being made. Democratic leaders listen and act on the opinions of the group. This type of management is good as it makes the employees happy and productivity is high. This is a very good method because employee’s thoughts and suggestions are listened to by the business.

This makes the employees seem as if they are respected and that their thoughts are valid. Coca Cola’s Management by Objectives Management by objectives is a process of management that emphasises the role of leadership and communications in the organisation and control of the business. It is a method of managing managers rather than the workforce at large. The following shows how Coca Cola is managed, by the three basic elements in management that Coca Cola uses by the objectives: • The identification of agreed goals by a manager and a subordinate • The definition of the subordinate’s responsibilities in terms of agreed results The use of agreed goals and responsibilities to control the progress of the business Cultures ——– Every business is made up of different cultures and the cultures that are present within the business depend on the management style and the organisational structures that are used. The different types of structures are: Role Culture- This is best suited to a hierarchy organizational structure. This type of culture works best by every employee playing the role that he or she has been predetermined and corresponds with the rules and regulations of the business

Task Culture- This culture encourages people to work as a team; this works best in a star structure. Power Culture- This works well in a matrix structure. It is based around one dominant individual/leader. Person Culture- this culture focuses on providing administrative help and support and close attention to one person in the organization. Role culture is the culture that Coca Cola adopts. This is where all members have a defined job or role to carry out. Role culture is normally split up into a number of functions that are organized in a hierarchical way.

Coca Cola would divide themselves into various functions like accounts, marketing and production. These also have hierarchical ordering of office examples of these are production director, production managers, supervisors, technicians, operatives etc. This type of culture works by logic and rationality. Role culture is mainly used in large organization. In this culture position in the main source of power and rules and procedures are the main source of influence. They also use task culture s the employees from the I. T department might have to work together to teach their goal or target Management style of Coca Cola.

If the culture of the business is not good, it can affect the number of absenteeism and punctuality. This means that if Coca Cola had a hard and unfriendly culture it can force their staff not to come to work because they might be picked on every day by other staff members, or they might not like the work they are given so they either come in late or take a day of work. This would result in the business losing out on work, and have less time to call in for a replacement. The culture of Coca Cola could have an affect on industrial relations, between managers and workers.

So if Coca Cola didn’t have a warm and genial culture it would cause more disagreements between staff and managers and staff would not be motivated to work, for example, staff may have to cut down on rest days, this could cause arguments as all staff would be tired from working everyday and would not have time to recover or spend it with their family. However, if the company had a warm culture then the managers and staff would get very well as staff would have less stress to compete with and would have a friendly environment to work in without having someone constantly shout out at staff. Advertisement

Haiyang Zheng Andrew Kusiak e-mail: andrew-kusiak@uiowa. edu Department of Mechanical and Industrial Engineering, 3131 Seamans Center, University of Iowa, Iowa City, IA 52242-1527 Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach In this paper, multivariate time series models were built to predict the power ramp rates of a wind farm. The power changes were predicted at 10 min intervals. Multivariate time series models were built with data-mining algorithms. Five different data-mining algorithms were tested using data collected at a wind farm.

The support vector machine regression algorithm performed best out of the ? ve algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10–60 min. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy of the power ramp rate. The data used in this research originated at a wind farm of 100 turbines. The test results of multivariate time series models were presented in this paper. Suggestions for future research were provided. DOI: 10. 1115/1. 142727 Keywords: power ramp rate prediction, wind farm, data-mining algorithms, multivariate time series model, parameter selection 1 Introduction Wind power generation is rapidly expanding and is becoming a noticeable contributor to the electric grid. The fact that most largescale wind farms were developed in recent years has made studies of their performance overdue. Given the changing nature of the wind regime, wind farm power varies across all time scales. The ? uctuating power of wind farms is usually balanced by the power produced by the traditional power plants to meet the grid requirements.

The change of power output in time is referred to as ramping and it is measured with the power ramp rate PRR . The prediction of PRR at 10 min intervals is of interest to the wind industry due to the tightening electric grid requirements 1 . Though the power prediction research has a long tradition in the wind industry, the interest in prediction of power ramps is emerging. There is no industry standard for PRR prediction. Power ramp rate on 10 min intervals is to bene? t the gird management and power scheduling in the wind industry. The literature related to power ramps is discussed next.

Svoboda et al. 2 proposed a Lagrangian relaxation method to solve hydrothermal generation scheduling problems. Three PRR constraints were considered and illustrated with a numerical example. Ummels et al. 3 presented a simulation method to evaluate the integration of large-scale wind farm power with the conventional power generation sources from a cost, reliability, and environmental perspective. Based on the PRR constraints for the reserve activation and generation schedule, the capability of a thermal generation system for balancing a wind power was investigated.

Potter and Negnevitsky 4 applied an adaptive-neuron-fuzzy inference approach to forecast short-term wind speed and direction. Torres et al. 5 used transformed data to build the autoregressive moving average ARMA time series model for prediction of mean hourly wind speed of up to 10 h into the future. Sfetsos 6 presented a novel method for forecasting mean hourly wind speed based on the time series analysis data and showed that the developed model outperformed the conventional forecasting models.

Lange and Focken 7 presented various models for short-term wind power prediction, including physics-based, fuzzy, and neuContributed by the Solar Energy Engineering Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received August 10, 2008; ? nal manuscript received March 6, 2009; published online July 9, 2009. Review conducted by Spyros Voutsinas. rofuzzy models. Using meteorological data, Barbounis et al. 8 constructed a local recurrent neural network model for long-term wind speed and power forecasting. Hourly wind farm forecasts of up to 72 h were produced.

Developing power and PRR prediction models for wind farms is challenging, as power output is known to undergo rapid variations due to changes in the wind speed, e. g. , due to gusts. The power output strongly depends on the wind conditions and the changing environment of the wind farm. The stochastic nature of a wind farm environment calls for new modeling approaches to accurately predict the power ramp rate. Data mining is a promising approach for modeling wind farm performance. Numerous applications of data mining in manufacturing, marketing, medical informatics, and energy industry proved successful 9–14 .

In this paper, a data-mining approach was applied to build a multivariate time series model to predict power ramp rates of a wind farm over 10 min intervals. Five different data-mining algorithms for the PRR prediction were employed. The boosting tree algorithm was used to reduce the dimensionality of the input and to enhance prediction accuracy. The models were built using historical data collected by the supervisory control and data acquisition SCADA system installed at a wind farm. 2 Basic Methodologies for PRR Prediction 2. 1 Time Series Prediction Modeling.

Time series prediction 15 focuses on determining future events based on known observations, measured typically at successive time intervals often uniform . Time series models are generally applicable to monitoring industrial processes and tracking time-based business metrics. There are two types of time series models: univariate and multivariate models. The univariate time series model consists of observations of a single parameter recorded sequentially over equal time increments. In the multivariate time series model, observations are ? xed-dimension vectors of different parameter values.

The univariate time series prediction model 15,16 is expressed as follows: ? y t + wT = f y t ,y t ? T , . . . ,y t ? mT 1 where T is the sampling time interval , wT is the prediction horizon for example, for w = 2 and T = 10 min, the prediction hori? zon is 20 min , y t + wT is the predicted parameter, y t , y t AUGUST 2009, Vol. 131 / 031011-1 Journal of Solar Energy Engineering Copyright © 2009 by ASME Downloaded 02 Sep 2009 to 128. 255. 53. 136. Redistribution subject to ASME license or copyright; see http://www. asme. org/terms/Terms_Use. cfm ? T , . . . y t ? mT are the current and past observed parameters, and m + 1 is the number of inputs predictors of the model. The multivariate time series model 15 is formulated as follows: ? y t + wT = f y t ,y t ? T , . . . ,y t ? mT ;x1 t ,x1 t ? T , . . . , x1 t ? mT ;x2 t ,x2 t ? T , . . . ,x2 t ? mT ; . . . ; xn t ,xn t ? T , . . . ,xn t ? mT 2 where T is the sampling time interval , wT is the prediction horizon, x1 . . . , xn , y and n + 1 are the observations of the time series ? forming the n + 1 dimensional vector, y t + wT is the predicted parameter, y t , y t ?

T , . . . , y t ? mT are the current and past observed values of y, x1 t , x1 t ? T , . . . , x1 t ? mT are the current and past observed values of parameters x1 , . . . , xn, and m + 1 n + 1 is the number of inputs predictors of the model. To obtain an accurate prediction model with the data-mining approach, appropriate parameters predictors need to be selected. Data mining offers different algorithms to perform this task. For example, the boosting tree algorithm 17,18 and the wrapper approach 19,20 , utilizing the genetic or the ? st best search algorithm 13,21 select the important predictors. The total number of all possible predictors m + 1 n+1 forms a high-dimensional input to the time series model, and therefore the performance of the resultant model is likely to be inferior. To maximize performance of the prediction model, a boosting tree algorithm is employed to select a set of the most important predictors among the m + 1 n + 1 ones in Eq. 2 : y t ,y t ? T , . . . ,y t ? mT ;x1 t ,x1 t ? T , . . . , x1 t ? mT ; . . . ;xn t ,xn t ? T , . . . ,xn t ? mT 2. 2 Prediction Accuracy Metrics.

Two main metrics, the mean absolute error MAE and the standard deviation Std of the absolute error AE , were used to measure prediction accuracy of different data-mining algorithms. The small value of MAE and Std imply the superior prediction performance of the models extracted by data-mining algorithms. In fact, MAE and Std based on absolute error are widely used in the wind industry. Their de? nitions are expressed as ? AE = y t + wT ? y t + wT N 3 Fig. 1 Typical power, power ramp rate, and wind speed plots: „a… wind farm power, „b… power ramp rate, and „c… wind speed AE i MAE =

N i=1 N 4 of each turbine is 1. 5 MW, the capacity of the wind farm is 133. 5 MW. The power ramp rate used in this paper is de? ned as the rate of change of wind farm power during a 10 min interval the standard time interval in wind energy industry and is expressed in kW/ min: PRR = P t + 10 ? P t 10 6 AE i ? MAE Std = i=1 N? 1 5 ? where y t + wT is the predicted PRR, y t + wT is the observed measured PRR, and N is the number of test data points for the prediction model. The data set used by the PRR prediction models is divided into training and test data sets. 2. 3 Data Description.

The data used in this research were generated at a wind farm with 100 turbines. Though the data were sampled at high frequency, e. g. , 2 s, it was averaged and stored at 10 min intervals referred to as the 10 min average data . The data used in this research were collected over a period of 1 month for all turbines of the wind farm. Some data contained many missing values or abnormal values outside of the normal physical range, and thus 89 turbines were selected for the study. For example, the SCADA recorded wind speed should be in the range 0–20 m/s, and the power should be in the range 0–1600 kW.

As the rated power 031011-2 / Vol. 131, AUGUST 2009 where P t + 10 is the wind farm power at time t + 10 time t plus 10 min and P t is the wind farm power at time t. The power ramp rate expresses the rate of change of the wind farm power due to the stochastic nature of the wind. Figure 1 a illustrates the power produced by a wind farm over 10 min intervals. Figure 1 b shows the power ramp rate corresponding to the power presented in Fig. 1 a . Figure 1 c shows the wind speed for the time period considered in Figs. 1 b and 1 c . Ignoring the power consumed by the wind farm, the power produced is always positive Fig. a ; however, the PRR can be positive or negative. The positive PRR indicates increasing power over time, while the negative PRR value means that the wind farm power is decreasTransactions of the ASME Downloaded 02 Sep 2009 to 128. 255. 53. 136. Redistribution subject to ASME license or copyright; see http://www. asme. org/terms/Terms_Use. cfm Table 1 List of parameters Parameter Mean Std Max Min Power PRR Description Mean wind speed of a turbine Standard deviation of the wind speed of a turbine Maximum wind speed of a turbine Minimum wind speed of a turbine Wind farm power Power ramp rate of the wind farm Unit

Table 3 The importance index of predictors generated by the boosting tree algorithm for t + 10 model Predictor m/s m/s m/s m/s kW kW/min PRR-1 PRR-2 PRR-3 PRR-4 PRR-5 Mean-1 Mean-2 Mean-3 Mean-4 Mean-5 Min-1 Min-2 Min-3 Min-4 Min-5 Max-1 Max-2 Max-3 Max-4 Max-5 Std-1 Std-2 Std-3 Std-4 Std-5 Power-1 Power-2 Power-3 Power-4 Power-5 Variable rank 100 100 66 53 71 44 49 38 41 37 67 52 49 44 42 45 48 37 42 40 43 51 45 43 36 40 54 48 41 39 Importance 1. 00 1. 00 0. 66 0. 53 0. 71 0. 44 0. 49 0. 38 0. 41 0. 37 0. 67 0. 52 0. 49 0. 44 0. 42 0. 45 0. 48 0. 37 0. 42 0. 40 0. 3 0. 51 0. 45 0. 43 0. 36 0. 40 0. 54 0. 48 0. 41 0. 39 Table 2 The data set description Data set 1 2 3 Start time stamp 1/1/07 1:40 a. m. End time stamp 1/31/07 11:50 p. m. Description Total data set; 4455 observations Training data set; 3568 1/1/07 1:40 a. m. 1/25/07 8:00 p. m. observations Test data set; 887 1/25/07 8:10 p. m. 1/31/07 11:50 p. m. observations ing. The larger the absolute value of PRR, the faster the power surge or drop . The wind speeds of 89 turbines, the wind speed statistics, and the power collected by the SCADA system were used in data mining.

In this paper, six different parameters were used to build the multivariate time series model. The mean, Std, max, min, and power are the ? rst ? ve parameters x1 , . . . , x5 and the PRR is the sixth parameter y of model 2 . Table 1 lists all the parameters used in this paper. The number of parameters is limited by the data available in this research. The model accuracy could be enhanced if more data were available. The six parameters recorded at 10 min intervals resulted in 4455 instances data set 1 in Table 2 , beginning from “1/1/07 at 1:40 a. m. ” and continuing to “1/31/07 at 11:50 p. . ” During this time period, the overall wind farm performance was considered to be normal. Data set 1 was divided into two subsets: data set 2 and data set 3. Data set 2 contains 3568 data points and were used to develop a prediction model with data-mining algorithms. Data set 3 includes 887 data points and were used to test the prediction performance of the model extracted from data set 2. For the test data set, the MAE Eq. 4 and Std Eq. 5 were the metrics used to evaluate the data-mining algorithms applied to learn multivariate time series model of Sec. 2. 1. 2. 4 Parameter Selection.

Due to the high-dimensionality of the input vector of predictors of the multivariate time series model, the number of inputs was reduced. The quality of the mod- Fig. 2 The importance of predictors generated by the boosting tree algorithm for the t + 10 model Journal of Solar Energy Engineering AUGUST 2009, Vol. 131 / 031011-3 Downloaded 02 Sep 2009 to 128. 255. 53. 136. Redistribution subject to ASME license or copyright; see http://www. asme. org/terms/Terms_Use. cfm Fig. 3 Illustration of the multiperiod multivariate time series prediction model: „a… the t + 10 min PRR prediction and „b… the t + 20 min PRR prediction ls learned from high- and reduced-dimensionality data were compared in Secs. 3. 1 and 3. 2. The most signi? cant predictors were determined by the boosting tree algorithm 17,18 . The same approach was shown to be successful in a previous research 14 . The basic idea of the boosting tree algorithm is to build a number of trees e. g. , binary trees splitting the data set and to approximate the underlying function. The importance of each predictor is measured by its contribution to the prediction accuracy of the training data set.

To build a multivariate t + 10 time series model for 10 min ahead predictions , the value of m = 5 used in the multivariate model is selected, which means that four values observed in the past and one current value of each parameter are considered. In total, six different parameters of the multivariate model were considered and thus it contains 5 6 = 30 predictors. The 30dimensional input is reduced by the boosting tree algorithm. Table 3 shows the importance index of 30 predictors computed by the boosting tree algorithm based on data set 2 of Table 2.

The index “-1” in Table 3 indicates the observation sampled 10 min earlier, “-2” indicates the observation sampled 20 min earlier, and “-3, -4, and -5” indicate the observations sampled 30 min, 40 min, and 50 min earlier, respectively. Note that all the parameter values used in this paper were all average values over the 10 min interval. Figure 2 shows the importance of all 30 predictors for the t + 10 min models ranked from the largest to the smallest one. To maximize prediction accuracy it is important to select important predictors among the ones on the list y t ,y t ? T , . . . ,y t ? mT ;x1 t ,x1 t ?

T , . . . , x1 t ? mT ; . . . ;xn t ,xn t ? T , . . . ,xn t ? mT A threshold value of 0. 50 was established heuristically to select the predictors for the time series models. The predictors selected by the boosting tree algorithm for the t + 10 min PRR are PPR-1, PPR-2, PPR-5, Min-1, PPR-3, Power-2, PRR-4, Min-2, and Std-2. The number of predictors was reduced from 30 to 9. The threshold value of 0. 50 used in the computation produced good quality results. A lower threshold value would lead to more Table 4 Prediction error of the t + 10 models without parameter selection generated by the ? e different algorithms Absolute error kW/min MLP SVM C&R Fig. 4 Prediction results produced by the t + 10 model without parameter selection: „a… prediction performance of the ? ve different algorithms for the test data set of Table 2 and „b… the observed and predicted PPRs by the SVM algorithm predictors that could degrade performance of the models due to the “curse of dimensionality” principle 19,22 , which means that high-dimension input could negatively impact performance of the model built by the data-mining algorithm. 2. 5 Multiperiod Predictions With a Multivariate Time Series Model.

The t + 10 min prediction model is not suf? cient for integration of the wind farm with the power grid. Six different multivariate time series models are needed to predict the PRR at t + 10– t + 60 min intervals. For t + 10 interval prediction, data set 2 in Table 2 is used for parameter selection and building time series models with data-mining algorithms, and the test data data set 3 in Table 2 were used to validate performance of the models. For t + 20– t + 60 predictions, the training data set remains the same; however, the test data set containing 887 points is reduced by one for each of the next 10 min period predictions.

Figure 3 illustrates the concept of a multiperiod prediction for PRR over 10 min intervals. In this model, the sampling time period T is 10 min. Using the 10 min average measured values including mean, Std, max, min, power, and PRR in Table 1 at the intervals t = ? 50, t = ? 40 , . . . , t = ? 10, t = 0? , the average PRR value at the subsequent interval t + 10 is predicted Fig. 3 a . In Table 5 Prediction error of the t + 10 model with selected parameters generated by ? ve different algorithms Absolute error kW/min MLP SVM C&R MAE 340. 66 298. 94 360. 19 396. 62 312. 44 Std 448. 9 323. 32 407. 56 396. 62 342. 33 Maximum 5119. 73 2512. 34 2657. 89 4236. 02 3516. 80 Minimum 0. 03 0. 15 0. 15 0. 38 0. 03 MAE 280. 13 243. 14 307. 97 356. 79 290. 57 Std 309. 38 276. 39 335. 56 323. 92 318. 37 Maximum 3248. 12 2817. 77 3860. 94 3516. 65 3270. 62 Minimum 0. 16 0. 03 0. 61 0. 15 0. 03 Random forest tree Pace regression Random forest tree Pace regression 031011-4 / Vol. 131, AUGUST 2009 Transactions of the ASME Downloaded 02 Sep 2009 to 128. 255. 53. 136. Redistribution subject to ASME license or copyright; see http://www. asme. org/terms/Terms_Use. cfm

Table 6 The importance index of predictors generated by the boosting tree algorithm for t + 20 model Predictor Mean-1 Mean-2 Mean-3 Mean-4 Mean-5 Std-1 Std-2 Std-3 Std-4 Std-5 Max-1 Max-2 Max-3 Max-4 Max-5 Min-1 Min-2 Min-3 Min-4 Min-5 PRR-1 PRR-2 PRR-3 PRR-4 PRR-5 Power-1 Power-2 Power-3 Power-4 Power-5 Variable rank 54 50 41 39 31 40 46 48 46 32 68 61 42 47 36 33 46 31 32 28 100 72 26 49 38 68 57 46 47 40 Importance 0. 54 0. 50 0. 41 0. 39 0. 31 0. 40 0. 46 0. 48 0. 46 0. 32 0. 68 0. 61 0. 42 0. 47 0. 36 0. 33 0. 38 0. 31 0. 32 0. 28 1. 00 0. 72 0. 26 0. 52 0. 38 0. 68 0. 57 0. 50 0. 51 0. 40 Fig. The prediction results of the t + 10 model with parameter selection: „a… prediction performance of the ? ve algorithms for the test data set of Table 2 and „b… observed and predicted PRRs by the SVM algorithm Fig. 3 b , based on the measured values including mean, Std, max, min, power, and PRR in Table 1 at the intervals t = ? 50, t = ? 40 , . . . , t = ? 10, t = 0 , the average PRR value at the subsequent interval t + 20 is predicted. Similarly, with the same input and different models, the 10 min average PRR values at intervals t + 30, t + 40, and t + 50 are predicted. 3 Industrial Case Study 3. The t + 10 min PRR Prediction Without Parameter Selection. To compare the accuracy of models built before and after parameters selection, the original 30 predictors were used as inputs to construct a multivariate time series model. Five different data-mining algorithms were applied to build PRR prediction models for a wind farm based on data set 2 of Table 2. These algorithms include the multilayer perceptron algorithm MLP 23,24 , the support vector machine SVM regression 25,26 , the random forest 27,28 , the classi? cation and regression C&R tree 13,29 , and the pace regression algorithm 13,30 .

The ? ve algorithms used in this research are representative of different classes of data-mining algorithms. The MLP algorithm is usually used in nonlinear regression and classi? cation modeling. The SVM is a supervised learning algorithm used in classi? cation and regression. It constructs a linear discriminant function that separates instances as widely as possible. The C&R tree builds a decision tree to predict either classes classi? cation or Gaussians regression . The random forest algorithm grows many classi? cation trees to classify a new object from an input vector. Each tree Fig. The importance of predictors computed by the boosting tree algorithm Journal of Solar Energy Engineering AUGUST 2009, Vol. 131 / 031011-5 Downloaded 02 Sep 2009 to 128. 255. 53. 136. Redistribution subject to ASME license or copyright; see http://www. asme. org/terms/Terms_Use. cfm Table 7 Prediction error for the t + 20 models generated by the ? ve different algorithms Absolute error kW/min MLP SVM C&R MAE 362. 52 301. 31 364. 28 336. 25 336. 79 Std 360. 21 319. 48 366. 12 340. 41 347. 08 Maximum 3960. 36 3635. 03 4067. 49 4473. 17 4023. 24 Minimum 1. 27 0. 10 0. 88 1. 34 0. 65 Random forest tree Pace regression otes for every class, and ? nally the forest chooses the classi? cation having the most votes over all the trees in the forest. The pace regression algorithm consists of a group of estimators that are either optimal overall or optimal under certain conditions. It is a new approach to ? tting linear models in high-dimensional spaces. To test the accuracy of these algorithms, models trained from data set 2 of Table 2 were tested on data set 3 from Table 2. Table 4 shows the prediction accuracy of the models generated by the ? ve algorithms. Figure 4 a illustrates the absolute error of different algorithms. The ? st 100 observed PPRs and those predicted by the SVM algorithm for data set 3 were shown in Fig. 4 b . It can be seen from Table 4 and Fig. 4 that the SVM algorithm outperforms the other four algorithms. The C&R tree algorithm produces the worst predictions, and the pace regression algorithm performs quite well. The model can be updated to re? ect the process change over time. The update frequency could be, e. g. , 3 weeks. Alternatively, a separate routine could monitor the model performance and refresh the model once its performance would degrade. 3. 2 The t + 10 min Prediction With Parameter Selection.

In this section, the predictors as input for the multivariate time series model are selected by the boosting tree algorithm. As described in Sec. 2. 3, 9 out of 30 predictors were selected to build the time series model. The nine selected predictors are PPR-1, PPR-2, PPR-5, Min-1, PPR-3, Power-2, PRR-4, Min-2, and Std-2. To test the difference between t + 10 min prediction models built with and without parameter selection, the ? ve data-mining algorithms in Sec. 3. 1 were used. Multivariate models were retrained from data set 2 of Table 2 and were tested on data set 3 from Table 2.

Table 5 shows the prediction accuracy of the models generated by the ? ve algorithms. Figure 5 a illustrates the absolute error of the ? ve algorithms, while Fig. 5 b shows the ? rst 100 observed PPRs and those predicted by the SVM algorithm for data set 3. The results in Tables 4 and 5, and Figs. 4 and 5 demonstrate that the prediction accuracy of all ? ve algorithms was improved after parameter selection by the boosting tree algorithm. The SVM algorithm outperformed the other four algorithms in both scenarios, i. e. , with and without parameter selection. 3. 3 The t + 20 min Prediction With Parameter Selection.

To build a multivariate time series model for t + 20 min PRR prediction, parameter selection is performed by the boosting tree algorithm. Table 6 shows the importance of 30 predictors computed by the boosting tree algorithm based on data set 2 in Table 2 and t + 20 prediction horizons. In Table 6, -1 denotes the observation sampled 10 min earlier, 2 denotes the observation sampled 20 min earlier, and -3, -4, and -5 denote the observations sampled 30 min, 40 min, and 50 min in the past, respectively. Figure 6 shows the importance index of the 30 predictors for t + 20 PRR predictions ranked from the largest to the smallest one.

When comparing the results in Figs. 6 and 2, and Tables 6 and 3, the importance of predictors varies for the t + 10 and t + 20 models. Similar to Sec. 2. 4, 0. 5 was established as a threshold to select signi? cant predictors for t + 20 model. The boosting tree algorithm selected seven predictors and provided the following ranking: PPR-1, PPR-2, Max-1, Power-1, Max-2, Power-2, and Mean-1. 031011-6 / Vol. 131, AUGUST 2009 Fig. 7 Observed and predicted PRRs from the t + 20 models with selected parameters: „a… MLP algorithm, „b… SVM algorithm, „c… random forest algorithm, „d… C&R tree algorithm, and „e… pace regression algorithm

Transactions of the ASME Downloaded 02 Sep 2009 to 128. 255. 53. 136. Redistribution subject to ASME license or copyright; see http://www. asme. org/terms/Terms_Use. cfm Table 8 Absolute error statistics for multiperiod models Absolute error kW/min t + 30 t + 40 t + 50 t + 60 min min min min prediction prediction prediction prediction MAE 329. 83 347. 92 387. 45 458. 70 Std 347. 03 418. 41 404. 92 469. 24 Maximum 4109. 27 4600. 32 4566. 47 4972. 20 Minimum 0. 59 1. 94 0. 02 0. 62 Table 7 shows the prediction error of the models generated by the ? e algorithms the same as in Sec. 3. 2 . Figure 7 shows the ? rst 100 observed and predicted PRR values for data set 3 in Table 2. The SVM algorithm outperformed the other four; however, the accuracy decreased compared with the t + 10 results reported in Sec. 3. 2. 3. 4 Multiperiod Prediction With Parameter Selection. As the SVM algorithm performed better for both t + 10 and t + 20 predictions. Therefore, it was selected to build multivariate time series PRR models for t + 30– t + 60 min intervals. After parameter selection with the same parameter importance threshold of 0. , the 30 predictors were reduced to a seven-dimensional input with the boosting tree algorithm. For the t + 30 min model, the seven predictors were ranked as follows: Min-3, Min-1, Min-2, PRR-2, PRR-3, Max-3, and PRR-1. For the t + 40 min model, the ranking is PRR-2, PRR-4, PRR-1, Max-1, Power-1, PRR-3, and Mean-1. For the t + 50 min model, the ranking is PRR-1, Max-1, Mean-1, PRR-3, Std-1, PRR-4, and Power-5. And for the t + 60 min model, the ranking is Std-2, PRR-2, Mean-2, Max-2, Power-4, Power-5, and Max-3. The boosting tree algorithm selects different parameters over different periods of the PRR prediction, i. . , the results depend on the data set properties. Using the selected parameters, multiperiod prediction models were built by the SVM algorithm. The test data set used for the t + 10 min model of Sec. 3. 2 containing 887 points was reduced by 1 for each of the next 10 min period predictions. Table 8 shows the absolute error statistics for the multivariate time series prediction over four different 10 min intervals. Figures 8 a –8 d show the ? rst 100 observed and predicted PRRs over t + 30 min, t + 40 min, t + 50 min, and t + 60 min intervals, respectively.

The mean, the standard deviation, and the maximum error all increase as the prediction horizon lengthens. However, the minimum error remains relatively stable. The multivariate model provides accurate PRR prediction at the t + 10 to t + 40 intervals; however, the accuracy at the t + 50 and t + 60 intervals deteriorates. It appears that for longer horizon predictions, weather forecasting data may be useful. 4 Conclusion In this paper, multivariate time series models for power ramp rate prediction at different time horizons, from 10 min to 60 min, were constructed.

Five different data-mining algorithms were used to build the PRR prediction models. The boosting tree algorithm selected important predictors. After parameter selection, the original 30-dimensional input was signi? cantly reduced, and thus the accuracy of the multivariate time series model was improved. The SVM algorithm outperformed the other four algorithms studied in this paper. The multivariate time series model for PRR prediction built by the SVM algorithm turned out to be accurate and robust. The models constructed in the paper predicted the power ramp at t + 10– t + 60 min intervals.

A comprehensive comparative analysis of the multivariate models built with different data-mining algorithms was reported in this paper. The time series models accurately predicted the power ramp rate of the wind farm at t + 10– t + 40 horizons; however, the accuracy at t + 50 min and t + 60 min horizons degrades. The extracted Journal of Solar Energy Engineering Fig. 8 Observed and predicted PRRs for different periods for the ? rst 100 test data points: „a… the t + 30 min PRR model, „b… the t + 40 min PRR model, „c… the t + 50 min PRR model, and „d… the t + 60 min PRR model models are essential in power grid integration and management.

The multivariate time series prediction model may become a basis for predictive control aimed at optimizing the power ramp rate. The current wind farm power prediction models usually estimate the power at 1 h or 3 h intervals based on weather forecastAUGUST 2009, Vol. 131 / 031011-7 Downloaded 02 Sep 2009 to 128. 255. 53. 136. Redistribution subject to ASME license or copyright; see http://www. asme. org/terms/Terms_Use. cfm ing data. These predictions reveal power ramps over long time horizons. Prediction of power ramp rates at shorter intervals, e. g. , 10 min, is of importance to the electric grid.

The model built in this research does not use weather forecasting data, and it provides valuable ramp rate prediction on 10 min intervals. One avenue to be pursued in future research is the transformation of the time series data, e. g. , using wavelets or Kalman ? lters. One disadvantage of the proposed approach is that the multivariate time series model used different parameters, and therefore updating the model with most current data is important. As the number of prediction steps increases, the error increases. The models investigated in this research were intended for predicting the power ramp rate at relatively short horizons.

One possible mitigation strategy is to incorporate weather forecasting and additional off-site observation data, all at additional computational cost. Other research questions, including the seasonal performance of the proposed approach, could be addressed, provided that the appropriate data would be available. Acknowledgment The research reported in the paper has been partially supported by funding from the Iowa Energy Center Grant No. 07-01. References 1 David, A. S. , 1994, Wind Turbine Technology: Fundamental Concepts of Wind Turbine Engineering, ASME, New York, p. 638. 2 Svoboda, A. J. , Tseng, C. , Li, C. , and Johnson, R. B. 1997, “Short-Term Resource Scheduling With Ramp Constraints,” IEEE Trans. Power Syst. , 12 1 , pp. 77–83. 3 Ummels, B. C. , Gibescu, M. , Pelgrum, E. , Kling, W. L. , and Brand, A. J. , 2007, “Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch,” IEEE Trans. Energy Convers. , 22 1 , pp. 44–51. 4 Potter, C. W. , and Negnevitsky, M. , 2006, “Very Short-Term Wind Forecasting for Tasmanian Power Generation,” IEEE Trans. Power Syst. , 21 2 , pp. 965– 972. 5 Torres, J. L. , Garcia, A. , De Blas, M. , and De Francisco, A. , 2005, “Forecast of Hourly Average Wind Speed With ARMA Models in Spain,” Sol. Energy, 79 1 , pp. 5–77. 6 Sfetsos, A. , 2002, “A Novel Approach for the Forecasting of the Mean Hourly Wind Speed Time Series,” Renewable Energy, 27 2 , pp. 163–174. 7 Lange, M. , and Focken, U. , 2006, Physical Approach to Short-Term Wind Power Prediction, Springer-Verlag, Berlin, p. 208. 8 Barbounis, T. G. , Theocharis, J. B. , Alexiadis, M. C. , and Dokopoulos, P. S. , 2006, “Long-Term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models,” IEEE Trans. Energ. Convers. , 21 1 , pp. 273–284. 9 Kusiak, A. , and Song, Z. , 2006, “Combustion Ef? ciency Optimization and Virtual Testing: A Data-Mining Approach,” IEEE Trans. Ind.

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Caretaker Government System must not Be Voided from the Constitution In the year 1996, under huge political pressure from the major opposition party the Awami league, the then government ruled by Bangladesh Nationalist Party (BNP) introduced the caretaker government system in Bangladesh constitution by making the 13th amendment.

The summary of the amendment was that the head of the Caretaker government will be called the Chief Adviser and will be selected by the President and the main objective of the caretaker government will be to create an environment in which an election can be held in a free and fair manner without any political influence of the outgoing government. After the amendment was made in the constitution, an election was held under caretaker government the same year and Awami league won the election. Justice Muhammad Habibur Rahman was the chief adviser at that time and led the election.

However, after two long term gaps, in 2008, when Awami league again became the government of the country, since then they are trying to demolish the 13th amendment from the constitution. From my point of view, caretaker government system must be voided from the constitution of Bangladesh as the present government is strong enough to conduct a peaceful and fair election, the Election Commission is very sure about giving clear and acceptable results and there would be no mishaps regarding elections as there would be more law enforcement people working and law implementation.

In 40 years of independence, Bangladesh has experienced the leadership of a few but major political parties. However, the matter of regret is that the existing political parties which successfully gained the trust of people could not gain the trust of each other. It is one of the sole reasons of introducing the caretaker government system in the constitution of Bangladesh. The cause is, as a lack of trust in each other, the political parties do not want to have the national election under the rule of another political party.

Noted Jurist Barrister Rafiqul haque in a press briefing clearly stated, “Holding the national election under a neutral caretaker government is a must for Bangladesh as both the major political parties do not have the trust in each other. ” He said, “The country’s political situation will be more volatile if the caretaker government system is dropped from the constitution. ” It is ironic that political parties who brought in the caretaker government provisions in the constitution in 1996 to ensure free and fair general elections have rendered the system controversial over the years.

As the major political parties do not have faith in each other, the caretaker government system is the only option for conducting a clear and peaceful election. If we look into the political history of Bangladesh we can see that whenever a parliamentary election has been hold by a political government and under its Election Commission, there was always a question of transparency and acceptability of election results. In one hand, where the present chief Election Commissioner, Mr.

ATM Shamsul Huda is deliberately saying in every press briefing, “ We are able to conduct a fair election and publish a authentic result”, the past few incidents regarding ‘Municipal and Union election’ shows the story of other hand. There have been enormous reports published in newspapers regarding ‘Municipal and Union election’ which have taken place very recently that the Election Commission’s authorized persons have intentionally made the persons winners who were the supporters of the present government.

Reports in the newspaper mentioned about places like Narayangonj, Bikrampur, Gopalgonj and some other also. If in ‘Municipal and Union’ elections, these types of incidents took place then surely the National parliamentary Election held under this Election Commission will be nothing but a stage show. At present, the whole statements of the Chief Election Commissioner have become self-contradictory. They are saying something in front of the public and doing something else behind (resource Lawaspect.com).

If this election commission conducts the National parliamentary Election, definitely the countrymen will never have fair and authentic results of the election and it will cause a number of disastrous incidents. Therefore, a caretaker government is immensely needed for fair and acceptable results in the elections of Bangladesh. The statement made by our Home Minister ADV. Sahara Khatun regarding our law enforcement departments was that the Police is doing a great job. According to her, the police is successfully controlling all the mishaps and doing no arm to the people. What about the people who speak on behalf of the people? On the strike of 10th July 2011 for caretaker government, called upon by the opposition parties, all the people of Bangladesh and people all over saw how opposition party’s chief whip Jainal Abedin Faruk was almost beaten to death. He was pulled vigorously in the streets by the police. When he tried to save his skin and ran, all the police on the scene again got all over him and started to kick him and beat him with sticks.

The intellectuals, the civil society all mentioned it as a disgrace to the country’s reputation. Here lies the story of our police force and the government when the opposition party is speaking on behalf of the people for caretaker government. This is truly unexpected and in a way this incident violates the democratic rights of a country. Another story of success I would like to write about is the opposition parties of our country, who are now speaking highly in support of caretaker government.

For re-establishing the caretaker government system, the main opposition parties are now calling on strikes every now and then. Undoubtedly, they do it on behalf of the people and for the betterment of country’s political future and condition of people. However, they forget what common people go through when everything is closed. Day laborers, whose earning depends on a day’s work, cannot manage the necessary items for their families. 24 hours strike means, the country is fully closed for 24 hours.

This includes market of common goods too. As a result, the prices of commodities rise up, making it beyond the reach of the hard life of common people. The two stories of success which I mentioned above are not supposed to take any support of any political parties. The main reason was to state that in every way these two incidents are unexpected and disgraceful for the country’s political history. The point is all these stories leads to the cause of one thing and one thing only and that is re-establishing the caretaker government system.

Therefore, in order to minimize these kinds of incidents caretaker government system must be re-introduced. In Democracy, all in all there is truly no place for systems like caretaker government. However, the fact is, our country has gone through so much political instability and crisis that none of the political parties have any positive attitude and support for each other. M. Abdul Latif Mondal, former secretary, said that people will remember how Awami League agiated for introducing the caretaker government system in Bangladesh with relentless ‘Hartals’.

Now, they are just turning their back to it and want to do the national election under their supervision. They did not even try to give any solid reasons why they want to demolish the system of caretaker government. The Supreme Court in suggestion clearly stated that for the sake of ‘peace’ and public interest, elections ‘may’ be held under the 13th amendment for the next two terms. Sadly, the government did not bother to take the suggestion. Dr.

Kamal Hossain, renowned law maker of the country commented it as a sign of insincerity and political opportunism of the present government. All these actions of the ruling party sent the opposition parties to the brink where they have no choice but to stand up for reasons. In addition, the inexplicable hurry to do away with the caretaker government, even though the next election is two and a half years away, raises questions and doubts about the government.

The caretaker government system was formed in Bangladesh only to conduct the election by 90 days. It has no power to bring any change in the constitution nor, to change the laws of the country but to do the normal daily works of the government and maintain the protocols. Therefore, for the sake of public trust and a transparent election, the caretaker government system must go on. As a democratic country, every political party in Bangladesh along with the people has the right to say for or against any issue of the country.

Therefore, if the major political parties, intellectuals are opposing the decision of government regarding voiding the caretaker government system from the constitution of Bangladesh, it has to be re-considered again. It does not have to be a one-sided decision by the government as it is a matter of a country’s political future and stability. Therefore, the caretaker government system must be continued for a peaceful and fair election, for clear and satisfactory results of the election and for minimizing unexpected incidents. References Karim Tariq A. (January 2007).

Bangladesh at the Crossroads. United States Institute of Peace. http://www. usip. org/files/resources/sr181. pdf M. Abdul Latif Mondal (December 23, 2004). Political Parties and Non-party Caretaker Government. [Electronic version]. EBSCOHost. Dr. Kamal (March 22, 2011). Caretaker Govt. should stay. Daily New Age. Hasan Ur Rashid (April 9, 2011) Do we need a caretaker government? Editorial. The Daily Star. Barrister Rafiqul Haque (April 22, 2011), in his own words [television series episode]. Tritio Matra. Channel I. http://www. banglapedia. org/httpdocs/HT/C_0041. HTML

Motivation is the act or process of motivating; a stimulus, influence, incentive, or drive. Motivation occurs rather its internal or external. Motivation is a way to encourage us to achieve our goals. A reward or incentive to push someone to complete their goal. A drive within yourself (personal or business) to complete your goal. For example, I know if I get my degree I will earn more money and have better hours at work. Internal and external factors play a big role in motivation and will get you to the next level and bring much success.

External factors of motivation are good for the people who don’t have much will power within themselves to accomplish a goal. Receiving a reward or incentive alter a goal is completed will keep them motivated and continue to strive to become successful. Motivation is a type of energy that keeps us going and trying to reach our goal. Internal and external sources are complementary in order for motivation to occur. Without motivation there’s not a will or a want to get the job done rather its internal or external. Motivation is the key to success and rewards.

I feel people need motivation regardless if its something they like or dislike because motivation is what keeps us going; when other people say we can’t do this or do that. Motivation gives the person that extra push or boost that they need to get the job done. The relationship between motivation and behavior goes hand in hand. Depending on how one’s behavior is will determine how motivated they are. For instance if someone have a low self esteem they probably won’t be as motivated as someone who self esteem is high.

A person that is very confident and achieve many other goals will be motivated by these factors. Some people can be self motivated and others need to be motivated by another person. A person with low self esteem will need to be motivated by another person because they will be unable to motivate themselves. Working at the juvenile facility I see many different behaviors. The clients there are motivated by different things drawing, writing rap songs or being competitive with each other. The client use more of external factors to be motivated.

As a team leader I use different client behavior to motivate them to do better or making better decision. For example, if I have several clients that are acting up; I will separate them from other clients and counsel them on their behavior. I will setup an agreement for them to sign and if they abide by it, they will receive extra time on phone call; outside recreation or be a captain throughout the shift. The clients are motivated through external factors to maintain a good behavior. Giving them privileges by rewarding them will motivate them to change their behavior and start acting good.

Motivation is exhibit in behavior if a person is motivated by money, they will work hard to be successful at making money. I am motivated to get my degree so I can be more of an assistance to the problems that juveniles faces today. My behavior is focus on reaching that goal and motivating myself more even when I am tired or feel like it is too much to deal with. Another example using one of the clients; they have a release date and it can change due to good or bad behavior. Having a good behavior the client may go home in 5 months, and bad behavior can be extended to 8 months.

Most of the clients strive for the 5 month goal so they can keep a positive attitude and try to stay away from negativity or other things that increase the length of their stay. Motivation plays a big role in all our lives whether its internal or external. There are a lot of things that can motivate us and people are motivated in different ways. Some people can motivate themselves and others need that extra boost from another individual, but either way motivation can change the way a person act or get the job done. Being motivated makes us all successful when our goal is accomplished.

Mobile Operating Systems (Mobile OS) Introduction: A mobile operating system, also called a mobile OS, is an operating system that is specifically designed to run on mobile devices such as mobile phones, smartphones, PDAs, tablet computers and other handheld devices. Much like the Linux or Windows operating system controls your desktop or laptop computer, a mobile operating system is the software platform on top of which other programs can run on mobile devices.

The operating system is responsible for determining the functions and features available on your device, such as thumbwheel, keyboards, WAP, synchronization with applications, email, text messaging and more. The mobile OS will also determine which third-party applications (mobile apps) can be used on your device. Types of Mobile Operating Systems There are various types of MOS. When you purchase a mobile device the manufacturer will have chosen the operating system for that specific device.

Often, you will want to learn about the mobile operating system before you purchase a device to ensure compatibility and support for the mobile applications you want to use. Among the various MOS here we discuss a few that are popular in market now. Android OS (Google Inc. ): The Android mobile operating system is Google’s open and free software stack that includes an operating system, middleware and also key applications for use on mobile devices, including smartphones.

Updates for the open source Android mobile operating system have been developed under “dessert-inspired” codenames (Cupcake, Donut, Eclair, Gingerbread, Honeycomb, Ice Cream Sandwich) with each new version arriving in alphabetical order with new enhancements and improvements. Bada (Samsung Electronics): Bada is a proprietary Samsung mobile OS that was first launched in 2010. The Samsung Wave was the first smartphone to use this mobile OS. Bada provides mobile features such as multipoint-touch, 3D graphics and of course, application downloads and installation. BlackBerry OS (Research In Motion) :

The BlackBerry OS is a proprietary mobile operating system developed by Research In Motion for use on the company’s popular BlackBerry handheld devices. The BlackBerry platform is popular with corporate users as it offers synchronization with Microsoft Exchange, Lotus Domino, Novell GroupWise email and other business software, when used with the BlackBerry Enterprise Server. iPhone OS / iOS (Apple) : Apple’s iPhone OS was originally developed for use on its iPhone devices. Now, the mobile operating system is referred to as iOS and is supported on a number of Apple devices including the iPhone, iPad, iPad 2 and iPod Touch.

The iOS mobile operating system is available only on Apple’s own manufactured devices as the company does not license the OS for third-party hardware. Apple iOS is derived from Apple’s Mac OS X operating system. MeeGo OS (Nokia and Intel) : A joint open source mobile operating system which is the result of merging two products based on open source technologies: Maemo (Nokia) and Moblin (Intel). MeeGo is a mobile OS designed to work on a number of devices including smartphones, netbooks, tablets, in-vehicle information systems and various devices using Intel Atom and ARMv7 architectures.

Palm OS (Garnet OS) : The Palm OS is a proprietary mobile operating system (PDA operating system) that was originally released in 1996 on the Pilot 1000 handheld. Newer versions of the Palm OS have added support for expansion ports, new processors, external memory cards, improved security and support for ARM processors and smartphones. Palm OS 5 was extended to provide support for a broad range of screen resolutions, wireless connections and enhanced multimedia capabilities and is called Garnet OS. Symbian OS (Nokia) :

Symbian is a mobile operating system (OS) targeted at mobile phones that offers a high-level of integration with communication and personal information management (PIM) functionality. Symbian OS combines middleware with wireless communications through an integrated mailbox and the integration of Java and PIM functionality (agenda and contacts). Nokia has made the Symbian platform available under an alternative, open and direct model, to work with some OEMs and the small community of platform development collaborators. Nokia does not maintain Symbian as an open source development project. ebOS (Palm/HP) : WebOS is a mobile operating system that runs on the Linux kernel. WebOS was initially developed by Palm as the successor to its Palm OS mobile operating system. It is a proprietary Mobile OS which was eventually acquired by HP and now referred to as webOS (lower-case w) in HP literature. HP uses webOS in a number of devices including several smartphones and HP TouchPads. HP has pushed its webOS into the enterprise mobile market by focusing on improving security features and management with the release of webOS 3. x.

HP has also announced plans for a version of webOS to run within the Microsoft Windows operating system and to be installed on all HP desktop and notebook computers in 2012. Windows Mobile (Windows Phone 7) : Windows Mobile is Microsoft’s mobile operating system used in smartphones and mobile devices – with or without touchscreens. The Mobile OS is based on the Windows CE 5. 2 kernel. In 2010 Microsoft announced a new smartphone platform called Windows Phone 7. Advantages of MOS: They can do more than a feature phone and can have higher end hardware which a feature phone can’t have.

Instead of Java apps, you get better, higher quality apps on smartphones. They provide richer functions and are more advanced and internet works more better on them. Simply, a smartphone is like computer in your pocket. Android, iOS, Symbian and Windows Phone are all smartphone OSs so they can do more than what a typical feature phone can do. Disadvantages: Smartphones (which is a phone that runs on an OS and can do more than a feature phone) are more vulnerable to viruses and they consume more power which results in a shorter battery life.