How did leaders impact the civil rights - Free Essay Example

Sample details Pages: 2 Words: 739 Downloads: 5 Date added: 2019/03/22 Category History Essay Level High school Tags: Civil Rights Movement Essay Did you like this example? Purpose Statement The purpose of this paper is to explain the events and leaders that impacted the Civil Rights Movement. I became interested in this idea when I first started learning about it in social studies, I then decided to go very deep into it and It really interested me. Don’t waste time! Our writers will create an original "How did leaders impact the civil rights?" essay for you Create order The information gained from this paper will help people understand the past and be able to learn from their mistakes. This information can help people grow into better versions of themselves. Therefore, I wanted to research and find out more information leaders and their impact on the Civil Rights Movement. Methodology Statement I began by scouring the internet and found the Civil Rights Movement and how long it was on The New Georgia Encyclopedia. I then, read the article and found out about the different people who helped and could be classified as leaders in the movement. I searched up each of their names and found articles on them, such as Martin Luther King Jr., John Lewis, and Malcolm X (etc.). I asked myself questions like; who are they? How do they show leadership? What did they do to impact the civil right movement? I began researching those question and came up with answers. Introduction According to The New Dictionary of Culture and Literacy the Civil Rights Movement is known as The national effort made by African Americans and their supporters in the 1950s and 1960s to eliminate segregation and gain equal rights (2005). Thats the dictionary definition of the Civil Rights Movement but, it means a lot of different things to a lot of different people. For example; for African Americans it was a time where they were fighting for equality, for white supremacists it was a time of fighting and hatred. There were those people who stood out among the rest and took up the role of a leader to gain rights and equality. There were those people who stood up for what they believed in and fought to the bitter end. Some of them didnt even see their goal accomplished but, they still tried. Research/Findings When people think Civil Rights, whats the first thing that pops into their head? Martin Luther King Jr. Well at least for most. For some people its segregation or other leaders. Leaders in this time were all shapes, sizes, and genders. Rosa Parks for example. John Lewis is another. John Lewis was always risking everything, including his life, for the Civil Right Movement. He fought battles, real and figuratively, always being arrested. He was part of Freedom Rides, which is where he and a small group of people would protest against the busses. Soon after, in 1963, he was given a place in the SNCC. This is a Civil Rights organization. Now he is a U.S. Representative for Georgias 5th congressional district. (Georgia Humanities and the University of the Georgia Press, 2004-2018) Elaine Brown is another great example. Elaine Brown was a chairwomen in the Black Panther Party. The Black Panther Party was made by Bobby Seale and was called the Black Panther Part only for self-defense. Elaine Brown was first introduced to the Black Panther Party when she started writing for the Black Congress Newspaper. They only then started to take notice of her when appeals for the Huey Newton Legal Defense Fund came in. It was long after in the end of April 1968 she went to her first official Black Panther meeting. She became the editor for The Black Panther. This was a huge achievement for her because after that she became the first elected female of the Panther central community. She helped lead other to do the same. (In the article, Brown, Elaine 1941-, 2017-2018). Martin Luther King Jr. A man who helped everyone and anyone. He believed the way to get his and others rights was to have peaceful protests and no violence. He participated from anything to protests to boycotts. After Rosa Parks refused to give up her seat on the public Montgomery bus people started to plan a boycott. Martin being the leader he was joined along with boycott. Originally the boycott was supposed to be one day but, it ended up being 381 days. Before the boycott Martin had been asked to join the MIA. He agreed and ended up leading the whole thing. The boycott still continued even though lots of people were being arrested.(Georgia Humanities and The University of the Georgia Press, 2004, 2018).

The Fundamental Principles Of Buddhism And Islam - 1585 Words

The following paper is going to discuss and describe the fundamental principles of Buddhism and Islam, consider the common and distinctive attributes and outline their influence and presence in modern Asia. The notion of religion is the fundamental foundation, and later the central body, for all past, present and future societies and cultures. The majority of the world’s population fabricates their own unique identity through the values and morals of the religion with which they follow. The present day allows opportunity for individuals to exercise their religious beliefs openly and with an absence of discrimination and judgment. Personal beliefs provide guidance and structure in the day-to-day lifestyles of those who engage with religion, influencing life choices. Throughout history it is evident that religion has been utilised as a means to justify actions along with the restraint of individuals. (Walker, 2012) Buddhism is an avenue of practice and spiritual development leading to an insight into the true nature of reality. The experience within the Buddhist tradition, over countless generations has created an incomparable resource for all those who wish to follow a path – a path that ultimately culminates in Nirvana of Enlightenment. (Thebuddhistcentre.com, n.d.) Varying to such is the Islamic faith, a phenomenon where all are called to the submission to the good will of God. The Islamic faith values two main concepts, involving strict monotheism and the importance ofShow MoreRelatedChristianity, Hinduism, Buddhism, And Judaism1644 Words   |  7 Pages The following religions Christianity, Islam, Hinduism, Buddhism and Judaism are among the top religions when evaluating the number of followers they encompass worldwide (Henderson, 2005, p.1). Through assessing these major belief systems and their views, diversity between them is apparent. These multiplicities range from Hindus who acknowledge multitudes of gods to Muslims who believe in one God, Allah. Although the variety of beliefs and practices exist their remains some central concepts suchRead MoreSimilarities Between Christianity And Islam1409 Words   |  6 Pages Christianity and Islam were both spread in socially and economically similar and politically different. They both had an important people that role and spread of the religions. Christianity included the individuals of who believe in Jesus Christ. Christians are the flowers of the Christ who often believed is the son of the God (â€Å"the father†); Christians strongly believe that Christ will return after the next life; the end of the world. Islam consists of individuals who believe in Allah, the godRead MoreIslamic Extremism And Religious Extremism1655 Words   |  7 PagesIn the religions of Christianity, Buddhism and Islam, fundamentalist groups are responsible for acts of religious extremism, committing violent crimes in the name of their religion. Although not as common in the present day as in history, Christian fundamentalism continues to exist in the world, and manifests itself in many groups. It is important to note that most, if not all acts of terror committed by Christian extremists are in opposition to the fundamental teachings of love and compassionRead MoreInfluence Of Religion On International Trade Essay1394 Words   |  6 Pagesclose for business). This research paper will discuss the influence of religion on international trade and also the impact that most influential religions such as Christianity, Hinduism, Judaism, Buddhism, and Islam have on trade. Religion has a significant role international trade. It set fundamental principles and values which govern the way its adherent behave and influence their daily decisions. Examples abound of religious tenets, holidays, and practices directly impacting the way people of differentRead MoreThe Pillar Of Religion Forms1444 Words   |  6 PagesThe pillar of religion forms one of the essential cornerstones of societal living that provides doctrines and guiding principles about how people should live and interact. The command of virtually all religions is often inclined on the preservation of peace so that every human being can live amicably next to their neighbor. The message of peace is often preached during the eruption of wars and conflicts where the existence of this virtue is nearly negligible. The numerous religions that exist globallyRead Moreworld view chart writing assignment Eddie Lundy Essay1707 Words   |  7 Pagesperson’s life.It is our Karma that according to Jaini sm determines the quality of our life. (Sivananda, 2004) Buddhism also views god and evil as innate and inseparable aspects of life. According to this view one particular individual cannot be labelled as fundamentally good or bad. A human being is capable of committing the greatest good as well as the worst of the evils. Good and Evil in Buddhism are not viewed as absolute, but as relative. Good and Evil are understood according to its consequences inRead MoreChristianity And Buddhism And Christianity1468 Words   |  6 PagesChristianity and Buddhism exist as two of the predominant religions throughout the world. While Buddhism ranks around fifth in number of followers of religions, it is the third most widespread religion behind Christianity and Islam. Buddhism and Christianity are arrantly distinct in their principle standpoints: Buddhism rejects the existence of a greater being and Christianity proudly professes the power of a universal God. However, despite this rigid dissimilarity, both religions developed and spreadRead MoreChrist ianity, Judaism, Hinduism, Buddhism, Confucianism, And Confucianism1472 Words   |  6 Pagesmeanings, ranging from â€Å"kind of similar if you look at it in the right fashion†, to â€Å"exactly alike†. In any case, there are many examples, and counterexamples of universal ideas between the â€Å"main† seven religions: Christianity, Judaism, Islam, Hinduism, Buddhism, Daoism, and Confucianism, which will be explored in the remainder of this essay. Arguments could be made on how all religions share a similarity. For example, all religions were persecuted by outsiders at some point. Perhaps the most dramaticRead MoreCcp Essay1083 Words   |  5 PagesThe church was also willing to work with local official requests so long as they do not violate principles of their faith. This â€Å"cooperative resistance† highlights the extremely pragmatic methods the government uses to maintain religious organisations within the country (Lu 2012:4). Despite this pragmatism, the Chinese government still discriminates heavily towards Christianity, Islam, and Tibetan Buddhism. The government still views Christianity as a foreign influence and recent efforts under Xi Jinping’sRead MoreReligious Philosophies and the Meaning of Life1701 Words   |  7 Pages through, billions of people turn to religion to help them answer these fundamental questions. In the Eastern tradition, there are three major religions/philosophies that evolved to help explain the meaning of life and the afterlife. . Hinduism originated in the Indian subcontinent, Buddhism in Nepal and northern India, and Confucianism in China. All three are similar in that they are not monotheistic in the manner of Islam or Christianity, but rather adhere to a set of beliefs that are more philosophical

Sociology of Sports †Baseball Free Essays

There are several theoretical perspectives of looking at sports from the sociology of sports view including conflict, functionalist, interactions, and feminist. The most useful though, in looking at the sociology of sports is the functionalist view. Functionalist regard sports as an almost religious institution that uses ritual and ceremony to reinforce the common values of a society (Ekern, 2013). We will write a custom essay sample on Sociology of Sports – Baseball or any similar topic only for you Order Now This means that functionalist view sports by the competition and patriotism of the younger generation and assist in maintaining a person’s physical condition. Not only do sports function as a safety valve for the viewers and the athletes for shedding destructive and tension energy in a way that is socially acceptable, but sports also assist in the joining of members of a community. The functionalist view seems to be the most appropriate over the other views in examining the sociology of sports. There are many reasons why the functionalist view is the most useful to use in viewing the sociology of sports. A big reason why the functionalist view is better is because many small communities spread throughout the world are built upon sports, especially in small communities that are not near any big cities. Sports are all they have in common and motivate the communities. Another reason is that the spectators and athletes always act in a different manner when they are either watching the sport or playing it, which is usually in an aggressive manner. When they are not watching the sport or discussing it they are usually completely different people. The last good reason is that it does help to maintain a person’s physical appearance, whether it is athletes staying in shape so they can be in top condition to compete or people that are ran by the idea of sports and want to stay in shape just like the athletes do. Sociology of sports, also referred to as sports sociology, is the study of the relationship between sports and society (Crossman, 2013). Aside from the functionalist view on sports sociology there other areas of study that are closely looked at, such as sports and gender, sports and media, and sports and gender and identity. Sports and gender targets man and women playing sports. Women were not even allowed to play sports until after the 1930’s because it was considered too masculine for them. Even in today’s age you never hear of women playing football or hockey, except maybe as a kid at school, and that is even very rare. Nowadays though in some sports they have a men’s team and a women’s team, such as basketball. There are even women that train in wrestling and boxing. The media is also another study of the sociology of sports that plays the games on the television. While the media will cover the men on football, basketball, baseball, hockey, boxing and pro wrestling, the sports that are usually covered for the women are figure skating, diving, gymnastics, and skiing. The media will also keep the audiences informed of player’s accomplishments and achievements. The gender identity of sports is another topic that often looked at because of sports having gender specific roles that is acceptable by society. Women are generally always treated more harshly in sports than men especially at younger ages. One reason that sports engage scholarly interests is because of the teaching of important values (Lewis, 2008). Sports sociology has studied higher education and sport and sport as a functional alternative to religion. Gender and racial discrimination in sports have also been studied, as well as social mobility on the basis of sport success that includes race and gender Another area that has been studied is the social problems of the sport that includes drugs, sports violence, and injuries. As you can see, all areas and aspects of sports, whether it was in the past or present day have been studied and as always to any debate everyone has a different view and perspective. There is no right or wrong answers to the sociology of sports, just opinions on different circumstances. The reason that people participate in sports, whether it is a fan, player, or a business is for the love of the sport and the profits. Some people just love the sport, while others try to make as big a profit as they can. Sports answer to a humans needs by providing a competitive entertaining experience. Sports can be explained from a sociological point of view as a necessity to most community’s different needs and circumstances. Baseball is a favorite pastime that dates as far back as 2000 B. C. with a ball and stick type games. However, the first rules were written by Alexander Cartwright, considered to be the Father of modern baseball (Penn, 2006). Alexander was part of the NY Knickerbockers, which was the first organization to play baseball in America that was established on September 23, 1845. In 1858, the National Association of Base Ball Players (NABBP) were created and known as the first baseball league. It wasn’t until 1860 though, that it was commonly referred to as â€Å"the National pastime† in several publications. Baseball is one of America’s most played sports and continually competitive against other teams and between individuals, especially in today’s age of baseball. The Sociodemographic description of fans and players of baseball is simple. The average MLB player rakes in over $2,000,000 a year if they are eligible for arbitration, which could come from disparate socioeconomic groups and from different countries. The average fan has an income of 30,000 to 40,000 a year and the high up luxury seats are for professionals that are in a very high income bracket and the corporate types. Being that baseball is very popular and has a very high income potential, it is definitely a professional sport. Baseball fits in to contemporary American life by giving fans something to always talk about. This sport gives society a certain set of values that fans and players live by. There are also culture trends that have impacted the sport of baseball. In fact, look at the roster of just about any Major League Baseball team, and you’ll find many of the most talented players coming from Latin American countries (Thomas, 2007). The media constantly reports on baseball, which gives the fans something to always talk, such as if they lost or won a game. If you look at the players, most of them are younger in age and usually are not over 40. All major league players are also male, which affect the views for the fans on the sport. As a beginning sociologist, the meaning of baseball as a social institution is very clear. Baseball is talked about, whether it is off season or every game, either loss or win, that gives fans something to always talk about. Over half of America enjoys this past time and every single one of them has their own opinion, depending on their team of choice and the opposing teams. Baseball is a sport that is great to attend in person, in order to get the full effect of the national past time. The baseball game that I had the chance of observing was the Rangers versus the Angels. The setting of the baseball field was split between the Ranger fans, which was a home game and the Angels fans. The field was a big diamond shape with for bases that the players have to run after a successful hit to the ball that the pitcher is throwing at them. There were also players from the opposing team that were stationed at each base and in the field closer to the stands. I notice that the environment changed drastically after each successful hit that made it close to the stands. Most of the fans would stand with excitement and all the players would scramble to get the ball and the player that hit it would try to run to as many bases before the is taken control of. The fans that were at the game that I attended were in their mid 20s to early 40s. The fans were a majority of men, but there were women there too. It seemed like a majority of the fans also wore their favorite player shirts and got excited every time the player hit the ball. It seemed that the fans age ration compared to the players were on the same levels. Most baseball player retires by time their 40, so the age comparison was evenly matched, from what I saw. The social behaviors that stood out were most fans would purchase a hot dog and beer from the concessions. It is always tradition to purchase a hot dog†, some of the fans would say. Fans would also be in groups for supporting their favorite teams. One special language and knowledge that characterizes the sport is Home Run. One particular behavior that I witnessed at the game was that when one team is winning the fans are excited and cheering and when their favorite team is losing they are booing and unhappy. Being at th e Rangers game made me aware of people’s behavior and surroundings and was a great experience to participate in. Sports use to be a big part of my life when I was a kid. I can remember collecting the Tops baseball cards with the hard piece of gum in it. It seems that every time I got some money I would go purchase the cards. Now that I look back, I realize that was my sociology behavior of being a fan. I also played baseball in school a lot, as well as other sports, such as football and basketball. When I was playing my attitude always changed to a competitive nature and I always got the mindset of crushing the opposing team. When I look back, I was two different people when I switched from a fan to a player and vice versa. The experiences of being a fan and a player as a kid didn’t have a huge impact to my adult life. I think that maybe that I played and watched sports too much as a kid, because I don’t have much interest in sports in my adult life. I read the newspaper to learn who wins and who is playing, but that is about it. I just learn enough to hold a conversation with my coworkers and friends and that’s it. So I guess the role that it plays in my life is that I played and watched it so much that it has caused me to lose interest in it. I can see how my experiences can connect me to others who are sports fans and participants. Sports are very big for kids in school and always have been. Most kids have shared the experiences that I have growing up. I’m not sure if they still sell Tops, but nonetheless kids are easily influenced and they do what everyone else is doing. Since so many adults like to watch sports and participate, it is a good assumption to say that most kids participate in some way or another, whether it is being a fan or participating in the sport itself. References Crossman, A. (2013). Sociology of Sports. Retrieved from http://sociology.about.com/od/Disciplines/a/Sociology-Of-Sports.htm. Ekern, J. (2013). Looking at Sports from Four Theoretical Perspectives. Retrieved from Article at Colorado Technical University Online. Lewis, J. (2008). Sociology of Sports. Retrieved from Kent State University at http://www.cengage.com/custom/enrichment_modules/data/0495598127_Sociology_of_Sports-mod_watermark.pdf. Penn, F. (2006). Early History of Baseball in America. Retrieved from Favorite Traditions. Com at http://www.favoritetraditions.com/baseball.html. Thomas, W. (2007). Sports – How Culture Impacts Our Choices. Retrieved from Ezine Articles at http://ezinearticles.com/?Sports—How-Culture-Impacts-Our-Choicesid=932334. How to cite Sociology of Sports – Baseball, Papers

Prediction of Wind Farm Power Ramp Rates a Data-Mining Approach free essay sample

Haiyang Zheng Andrew Kusiak e-mail: [emailprotected] 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 Par ameter 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 pow er 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 â€Å"1/1/07 at 1:40 a. m. † and continuing to â€Å"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 extracte d 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 tre es 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 â€Å"-1† in Table 3 indicates the observation sampled 10 min earlier, â€Å"-2† indicates the observation sampled 20 min earlier, and â€Å"-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 CR 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 â€Å"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 CR 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 Trans actions 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 CR 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 CR 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 CR 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 algorith m outperforms the other four algorithms. The CR 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†¦ CR 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. 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