A Research Project Report For Cs 6735 Essays

A Research Project Report For Cs 6735 Essays

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Machine Learning for
Predicting Power Generation by
Single Wind Turbine

Chirag Parmar
{cparmar@unb.ca}
University of New Brunswick, Fredericton Canada
A Research Project report for CS 6735


Abstract
Optimum use of renewable source of energy like Wind can be considered as one of the answer to rapid increase in the demand of energy worldwide. Using the current technological advancements it is commercially viable to use the wind power as a source of energy for small scale to large scale industry.All kind of renewable source are highly dependent on environmental conditions and can not be controlled fully. Due to this inherent characteristics, power generation may vary. In the electricity grid at any moment balance must be maintained between electricity consumption and generation - otherwise disturbances in power quality or supply may occur. Wind generation is a direct function of wind speed and, in contrast to conventional generation systems, is not easily dispatchable. Fluctuations of wind generation thus receive a great amount of attention.My research work focuses on building a short term(few hours) to medium term (up to 2-3 days) prediction model to predict how much power can be generated during a certain point of time from single wind turbine ,provide suggestion on maintenance activities , current research activities ,challenges and future developement in this field.

Introduction
Wind power industry is rapidly expanding .It is essential to predict power generation accurately. According to CanWea(Canadian Wind Energy Association), Across Canada, electricity generated from wind is already powering over 1.5 million homes and businesses in a clean,reliable and efficient manner. With Canada’s unparalleled wind resource, there are...


... middle of paper ...


...tya Choukulkar, Yelena Pichugina, Ronald Calhoun, Robert Banta, Alan Brewer, Michael Hardesty
Current methods and advances in forecasting of wind power generation
by Aoife M. Foley , Paul G. Leahy, Antonino Marvuglia, Eamon J. McKeogh
A Review of Wind Power and Wind Speed Forecasting Methods With Different Time Horizons Saurabh S. Soman, Hamidreza Zareipour, Senior Member, IEEE, Om Malik, Life Fellow, IEEE, and Paras Mandal, Member, IEEE
Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies Nicholas Steckler, Anthony Florita, Jie Zhang, Bri-Mathias Hodge,Transmission and Grid Integration Group,National Renewable Energy Laboratory Golden, Colorado, USA
Multiple input and multiple output simulation metamodeling using Bayesian networks by Poropudas, J.; Pousi, J.; Virtanen, K

Siemens Wind turbine - Machine learning
Makhani Kites, Google X

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