Business Ethics In Firm Rana Plaza - Free Solution

Questions: On April 24 2013, 1134 individuals were slaughtered and 2,500 were harmed when the Rana Plaza working in Savar, Bangladesh fallen on article of clothing laborers inside its manufacturing plants. It would be known as the most exceedingly awful mishap in the article of clothing industry anyplace. This happened just five months after a horrendous fire at a comparable office incited driving worldwide brands to promise to work to improve wellbeing in the countrys blasting however inadequately controlled piece of clothing industry. Work gatherings, Western dress organizations, the Bangladeshi government and others have gained some ground toward forestalling comparative disasters, however more stillneeds to be finished. There was never any uncertainty that improving working conditions in Bangladesh, one of the universes most unfortunate nations, would be unfathomably troublesome. The breakdown of RanaPlaza was only one, however by a long shot the most noticeably terrible in a progression of modern mishaps in Bangladeshs article of clothing industry, which has gotten probably the greatest exporter ofclothes to the United States and Europe due to its low wages. It was later found that 28 brands that sourced garments from the square included Primark, Bennetton, Mango, Matalan and Bonmache, inciting open worry about the working states of piece of clothing industrial facilities around the globe which add to western high road design stores. Moral commercialization urges individuals to consider how the items they purchase are sourced and created which are not hurtful to the earth and society.This can be confirm through essentially buying eggs that are unfenced or boycotting merchandise/organizations which advance kid work or offensive working conditions. Moral industrialism is a developing business sector. An ongoing report from the Co-employable Bank demonstrated 33% of UK purchasers professing to be worried about moral utilization, with an enormous number of the open ready to challenge and blacklist organizations which don't agree to moral guidelines. You are required to compose an expository business report that covers the accompanying assignments: Errand 1 Talk about the manners by which organizations, similar to the ones referenced in the concentrate over, that sourced garments from the Rana Plaza can help improve strategic approaches to forestall repeat of occasions of this nature. Assignment 2 Pick any organization, examination and proof how they work morally, considering their way to deal with commercialization, values and ecological benevolence. Answers: Errand 1 About the report: The exposition examinations firm named Rana Plaza inside the material business in Bangladesh. Initial segment of the examination has really attempted to investigate all the organizations who have sourced or are sourcing from the Rana square and the manner by which they can help themselves for development of strategic policies and avoidance of repeat of any such occurrence which occurred in Rana Plaza (Allhoff and Vaidya, 2005). Exploration likewise has additionally endeavored to assess every moral issue that were engaged with the firm just as all activities which related firms need and prerequisite to embrace further smooth continuance. Investigation and assessment of such moral just as social in addition to companywide issues will guarantee that organizations appropriately see every single thing which turned out badly in the Rana Plaza in addition to adequately get themselves far from any comparable occurrence or activities which could hurt others in comparative manner (Boylan, 2001 ). In the second section an association named Starbucks has been considered just as all exercises of the firm viewing key moral concern, for example, CSR and commercialization, ecological cordiality just as convictions and qualities has been perceived as such activities will move Starbucks objectives in addition to points into the future (Cory, 2005). Prologue to occurrence: The indispensable catastrophe occurred at the association Rana Plaza which is arranged inside Bangladesh, where an eight celebrated structure including six production lines actually fell like the place of cards, executing around 1,134 works. This case likewise has put a notification on wellbeing just as security issues in the material business (Gavai, 2010). This episode has constrained the organizations to reconsider upon the morals and resolve they convey and furthermore that whether the genuine morals towards wellbeing and security of representatives in being kept up or not. Likewise therefore, media, government just as customers in addition to likewise not many different partners even are increasingly considering dress organizations responsible for each wellbeing in addition to security repudiation inside their gracefully chains (Harish Jyawali, 2015). Conversation: As investors who have a place with material industry in addition to clothing firms are available to components of monetary just as reputational dangers in addition to even peril that comes out of episodes joined to risky working conditions at the organizations locales for creation. Thusly, financial specialists currently require conveying a huge bet in encouraging firms to execute legitimate wellbeing just as security activities and practices all through their self in addition to likewise their providers tasks (Hartman, 2005).Several factors in actuality share the risk of fiasco that occurred in the Rana Plaza. Bangladesh that is considered as a state having powerless administration even faces a few basic issues just as issues like absence of some certifiable principle and even guideline of the law,inadequate and extremely scant appraisal methodology, in addition to for the most part exceptionally ineffective affirmation towards equity for the survivors of the work-associated occurre nces. The firm would now be able to follow some basic advances and accomplish full wellbeing and security too as can get equipped for maintaining a strategic distance from any such occurrence that occurred at Rana Plaza (Mitchell, 2009). These means are: As the administration is feeble in this country firms must keep rules of permit and furthermore protections for every one of its staffs and partners The organizations must have a legitimate structure towards work The organizations must set down principles and guidelines for itself and must comply with every one of them and even cause its staffs to follow the same(Moon, 2001) Firms must attempt to enjoy a decent appraisal and assessment process where an ideal assessment of the structure and premises and furthermore everything being equal and instruments utilized in the destinations must be continued. The organizations must be moral with regards to giving wellbeing and security to staffs and furthermore should furnish every one of its staffs with great equity and equity (Morais et al., 2014). Working conditions planned for all the works must be appropriately improved and looked after from that point. Great natural measures must be kept up Scarcely any vitality sparing methods must be executed while creation on locales Representative wellbeing in addition to security norms require to be maintain and right plans for wellbeing just as security of workers should likewise be made In any case, in prompt results of the manufacturing plant catastrophe, hardly any organizations incredibly talked incomprehension identified with whether the industrial facilities are truly formed alongside their merchandise or, more than likely it is just a saying.Undoubtedly, as a result of worldwide associations which re-appropriated creation to the countries like Bangladesh, self-existing temporary workers regularly gave decisions inside complex in addition to crucial chains of gracefully (Paliwal, 2006). Firms here should attempt to comprehend that the expense in addition to their differentials, in actuality were genuine reasons for redistributing in the underlying spot, amidst completely created and furthermore barely any creating nations which demonstrated conditions inside the processing plants that were maybe unsatisfactory, with being only observational confirmation which could discredit such things. Suggestion: The brands in addition to firms can really sign Accord on Building Safety and staff security in Bangladesh which would even allow the staffs to stop work if by means of any source they feel that their wellbeing is underneath danger. Material industry likewise has not overlooked Rana Plaza case, and neither the purchasers have. It is in truth crucial to as often as possible pose inquiries in addition to search out the brands and firms which are wholeheartedly attempting to expand laborer conditions (Wurgaft, 2003). End: The significant obligation was upon Bangladeshi specialists since they bombed in accurately satisfying their obligations and impulses towards securing works and making them mindful of the occurrences and dangers and furthermore exercises towards sparing lives from those dangers. Condition of the Bangladesh, as per the national and universal law ought to very attempt its best to spare and secure all the human rights inside power since they even were fruitless in their duty which was tied in with giving the rights just as wellbeing to the material business workers. Undertaking 2 Presentation: Starbucks really is a worldwide espresso chain and café in situated in Seattle inside United States in Washington. Alongside right around 17133 outlets in 49 countries Starbucks is globes biggest café firm. Wellbeing in addition to security of purchasers and the laborers is significant factor of all the moral components that must be followed inside any association (Cole, 2008). Each firm notoriety essentially relies on wellbeing in addition to security of the purchasers. Wellbeing in addition to security is additionally legitimate necessity of government. Worldwide firms have representatives who have a place with a few countries accordingly every one of them should be esteemed a great deal for fulfillment of better outcomes (Snyder, 2006). System: The strategy utilized here for assortment of information is auxiliary information assortment technique. Data has been assembled from numerous optional assets like from the magazines, firms site, diaries, and even a few books. Conversation (Findings): Commercialization Starbucks social significance effectively persuades its purchasers to devour its items and furthermore this has served like a decent work environment just as mingle. Starbucks has an incredibly shared environment and is a perfect spot expected for creating just as keeping up great relations. Culture in addition to utilization which are a few structures towards industrialism has additionally molded star bucks outlets through advancement of the social relatio

E-Commerce in East Africa

List of chapters Definition and background2 Reasons for development of web based business in East Africa. 4 Influence of online business on exchanging rehearses East Africa. 5 Types of e-commerce8 Challenges confronting the development of web based business in East Africa. 9 1. Poor infrastructure9 Computer illiteracy9 Lack of legitimate regulation9 Inadequate capital10 Inadequate personnel10 Conclusion10 References11 Definition and backgroundE-trade alludes to business directed using PCs, phones, fax machines, standardized identification perusers, charge cards, robotized teller machines (ATM) or other electronic apparatuses (regardless of whether utilizing the web) without the trading of paper-based reports. It incorporates exercises, for example, acquirement, request section, exchange preparing, installment, confirmation and non-renouncement, stock control, request satisfaction, and client assistance. At the point when a purchaser pays with a bank card swiped through an attractive stripe-peruser, the individual in question is partaking in web based business. |It basically includes the purchasing and selling of items or administrations over electronic frameworks, for example, the Internet and other PC systems. Electronic business draws on such innovations as electronic subsidizes move, gracefully chain the board, Internet advertising, online exchange preparing, electronic information trade (EDI), stock administration frameworks, and computerized information assortment frameworks. Present day electronic business ordinarily utilizes the World Wide Web at any rate at one point in the exchange's life-cycle, in spite of the fact that it might incorporate a more extensive scope of advancements, for example, email, cell phones and phones as well.Originally, electronic trade was distinguished as the help of business exchanges electronically, utilizing innovation, for example, Electronic Data Interchange (EDI) and Electronic Funds Transfer (EFT). These were both presen ted in the late 1970s, permitting organizations to send business reports like buy requests or solicitations electronically. The development and acknowledgment of Visas, robotized teller machines (ATM) and phone banking during the 1980s were likewise types of electronic business. Another type of online business was the carrier reservation framework epitomized by Saber in the USA and Travicom in the UK.Electronic trade or web based business is a term for a business, or business exchange that includes the exchange of data over the Internet. It covers a scope of various sorts of organizations, from buyer based retail destinations, through closeout or music locales, to business trades exchanging products and enterprises between companies. It is right now one of the most significant parts of the Internet to develop. Online business permits buyers to electronically trade merchandise and ventures without any boundaries of time or distance.Electronic trade has extended quickly in the course of recent years and is anticipated to proceed because of current circumstances, or even quicken. Sooner rather than later the limits among â€Å"conventional† and â€Å"electronic† trade will turn out to be progressively obscured as an ever increasing number of organizations move areas of their tasks onto the Internet. Business to Business or B2B alludes to electronic trade between organizations as opposed to between a business and a shopper. B2B organizations regularly manage hundreds or even a great many different organizations, either as clients or suppliers.Carrying out these exchanges electronically gives immense upper hands over conventional strategies. At the point when executed appropriately, internet business is frequently quicker, less expensive and more helpful than the customary techniques for dealing products and enterprises. Electronic exchanges have been around for a long while as Electronic Data Interchange or EDI. EDI requires every provider and client to set up a committed information interface (between them), where internet business gives a practical technique to organizations to set up numerous, and specially appointed links.Electronic business has likewise prompted the improvement of electronic commercial centers where providers and potential clients are united to lead commonly useful exchange. Much the same as the remainder of the world, East Africa hasn’t been deserted in receiving web based business as a method of working together. Numerous people, partnerships and even governments have set out to utilizing web based business in fulfilling their business exchanges, but on a littler edge when contrasted with western nations or the more evolved world economies.Countries in east Africa, that is Kenya, Uganda and Tanzania have all been making strides in the ongoing past to guarantee that exchange among them develops as a method of boosting the monetary development of these nations. One of the means has obviously been re ceiving the utilization of online business. Applicable framework has been or is being set up to back up this reception. Since web based business in a perfect world is about the web, a large portion of the framework I am alluding to includes it in way or another.Most quite has been the laying of the fiber optic link from the shore of Kenya towards the inland that considers rapid web get to. Purposes behind development of web based business in East Africa. The quick development of online business since 1995 is because of the extraordinary highlights of the Internet and the Web as a business medium: * Ubiquity: Internet/Web innovation is all over the place, at work, home, and somewhere else, and whenever, giving an omnipresent market space, a commercial center expelled from a worldly and land area. * Global come to: The innovation comes to across national limits. All inclusive norms: There is one lot of Internet innovation guidelines, which extraordinarily lower showcase passage costs (the expenses to put up merchandise for sale to the public) and diminish search costs (the push to discover items) for the shopper. * Richness: Information lavishness alludes to the multifaceted nature and substance of a message. Web innovation takes into consideration rich video, sound, and instant messages to be conveyed to enormous quantities of individuals. * Interactivity: The innovation works through association with the client. * Information thickness: Information thickness is the aggregate sum and nature of data accessible to all market participants.Internet innovation lessens data expenses and raises nature of data, empowering value straightforwardness (the simplicity for shoppers of finding an assortment of costs) and cost straightforwardness (the capacity of buyers to decide the real expenses of items). Data thickness permits shippers to participate in value segregation (offering merchandise to focused gatherings at various costs). * Personalization/customization: E-busin ess advances grant personalization (focusing on close to home messages to shoppers) and customization (changing an item or administration dependent on buyer inclination or history.Influence of web based business on exchanging rehearses East Africa. As it has just been set up, web based business is being utilized, despite the fact that not all that broadly in East Africa. The organizations or associations that have chosen to utilize internet business are profiting by it in the accompanying manners: 1. Misuse of New Business Broadly, electronic trade stresses the age and abuse of new . business openings and to utilize well known expressions: â€Å"generate business value† or â€Å"do more with less† Safaricom, versatile specialist co-op has the m-pesa administration that caught such a significant number of clients and helped numerous individuals build up new businesses.There is additionally the m-kesho administration which is a joint endeavor among Safaricom and Equity B ank that has empowered numerous entrepreneurs and people to get to banking administrations. 2. Empowering the Customers Electronic Commerce is empowering the client to have an expanding state in what items are made, how items are made and how administrations are conveyed (development from a moderate request satisfaction process with small comprehension of what is occurring inside the firm, to a quicker and rt1ore open procedure with clients having more noteworthy control. . Improvement of Business Transaction Electronic Commerce tries to improve the execution of business exchange over different systems. 4. Compelling Performance It prompts increasingly powerful execution I. e. better quality, more prominent consumer loyalty and better corporate dynamic. 5. More prominent Economic Efficiency We may accomplish more prominent financial effectiveness (lower cost) and progressively quick trade (fast, quickened, or continuous cooperation) with the assistance of electronic business. 6. Exe cution of InformationIt empowers the execution of data loaded exchanges between two mineral more gatherings utilizing entomb associated systems. These systems can be a mix of ‘plain old phone system’ (POTS), Cable TV, rented lines and remote. Data based exchanges are making better approaches for working together and even new sorts of business. 7. Fusing Transaction Electronic Commerce likewise inco11'orates exchange the board, which composes, courses, procedures and tracks exchanges. It additionally incorporates shoppers making electronic installments and assets moves. 8.Increasing of Revenue Firm use innovation to either bring down working expenses or increment income. Electronic Commerce can possibly build income by making new markets for old items, making new data based items, and setting up new assistance conveyance channels to all the more likely serve and cooperate with clients. The exchange the board part of electronic trade can likewise empower firms to lessen w orking expenses by empowering better coordination in the business, creation and conveyance forms and to unite activities parched decrease overhead. . Decrease of Friction Electronic Commerce research and its related usage is to diminish the â€Å"friction† in on line exchanges grindings is frequently depicted in financial matters as exchange cost. It can emerge from wasteful market structures and wasteful blends of the innovative exercises required to make an exchange. At last, the decrease of grating in online trade wil

Massachusetts The Geek State

Massachusetts The Geek State The Forbes blogs broke an interesting story  early  last week that I went absolutely nuts over, and Im so glad to finally have enough time to blog about it. If, for some reason, you dont like following links to blogs that arent MIT Admissions blogs, Ill sum it up for you: The US National Science Foundation released a ton of statistics as part of their Science and Engineering Indicators report  for 2010. They compiled a lot of lists, but one of them ranked the top 20 metropolitan areas in the United States in order of the percentage of workers in STEM (Science, Technology, Engineering and Mathematics) occupations. As one Forbes blogger indicated, thats as good a measure as any of geekiness, at least with respect to the workforce. The top city, naturally, was San Jose, California, home of Silicon Valley and the headquarters of such companies like Apple, Google, HP, and eBay. In second place was Boulder, Colorado. (Not) surprisingly, Massachusetts fared very well in the rankings, boasting three cities that made it in the top 20. Boston came in 15th place with 10.3% of its workforce in STEM occupations and if you think about it, Boston is right across the river from where MIT is. The sixth geekiest city was Lowell, Massachusetts, with 14.1% of all working people in STEM occupations. Are you wondering what the third Massachusetts city is? Its none other than my hometown of Framingham, Massachusetts. And get this: we came in third place, with 16.6% of our workforce employed in STEM occupations. Why are we so geeky awesome? There must be something in the water. That might also explain why Im so good at making bad chemistry jokes! (..orrrrrr Ill just stick to blogging and continuing my full-time linguistics UROP. As I work from home. In the third geekiest city ever, mind you.)

Cultural Proximity And Cultural Distance - 1523 Words

Cultural Proximity and Cultural Distance As Japanese economy soared, its media products such as manga, TV shows, movies and music spread out across Asia. Especially, the young people in Asia began to embrace Japanese culture rather than the culture from the most dominant culture exporter- the United State, and this phenomenon was analyzed by Koichi Iwabuchi in his Feel Asian Modernities. His account of this intra-regionalization in Asia is cultural proximity that Japanese culture shares intimate similarities with other Asian countries and appeal to the audience to perceive this cultural flow.[ Iwabuchi, K(2004), Introduction: Cultural globalization and Asian media connections. Feeling Asian Modernities, pp 12.] In this way, American†¦show more content†¦For example, fans from other countries traveled to Japan to see and experience the music, food, urban lifestyle, fashion and other key attractions by themselves.[ Iwabuchi, K(2004), Feeling Asian Modernities, Hong Kong: Hong Kong University Press. pp 12.] However, not all of Japanese values were accepted by the other countries’ audience, and then Iwabuchi explains it with the concept of cultural distance in media practice. Singaporean women thought depiction of sexuality in the Japanese TV drama was unrealistic; Korean and Taiwanese people criticized the Japanese cultural flow into their countries as a cultural invasion.[ â€Å"Iwabuchi†,pp17] Therefore, the other Asian countries began producing their own TV drama on youth love affair and urban lifestyle for their way of representing Asian modernity and national identification. 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Security Policy Firms Need A Formal Security Program

†¢ Security policy: Firms need a formal security program which must be accompanied by formal executive support. This lays the groundwork for a successful information security program. Firms must also consider security policy as a living document that is subject to adjustments as the organization evolves. †¢ Security organization: Firms need a formal organizational structure in place to manage the overall security program. This will allow the firm to assign the correct roles and reponsibilities. This provides clarity for escalation processes and which resources will be involved in the event of a security breech. †¢ Asset classification and control: Firms need to have all their systems, networks, and devices identified so the right security controls can be applied. In the case of large organizations, an asset management system may be warranted to keep track of all the various assests of the company. †¢ Personnel security: Firms need to incorporate security into their overall business processes. Firms can expect their employees to adhere to reasonable security controls. Therefore, it may be necessary to vet employees within the hiring process such as screening and performing background checks. Monitoring employee system activity is necessary to ensure systems are secure. †¢ Physical and environmental security: The security program will involve protecting the physical hardware, buildings, and people. 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Possible Danger Signs on Dissertation Writing You Must Be Aware Of

Possible Danger Signs on Dissertation Writing You Must Be Aware Of There are a lot of important actions that should be followed in order guide write the ideal dissertation possible. Many internet courses will make beautiful environments in which you are able to play around with deep learning concepts. The students don't need to pay much in affording the help of online providers. SECONDARY RESEARCH Using information that was gathered by other people through their principal research is known as secondary research. Students ought to be able a lot of matters together When producing a dissertation about the subject of a person's alternative. Do comment in case you have any questions. Perhaps, it's the one principal explanation that we've been asking for. Before starting the dissertation you must know what sort of research you wish to conduct. Knowing what it is you are attempting to find out will help you keep focused in your research and analysis. 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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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