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http://hdl.handle.net/11718/14044
Title: | Socioeconomic Implications of Wind Energy – A Perspective from Gujarat |
Authors: | Purohit, Amit Pindoriya, Naran M. |
Keywords: | Power Trading;Regression Based Prediction;Renewable Energy Certificate;Power Forecasting |
Issue Date: | 2015 |
Publisher: | Indian Institute of Management, Ahmedabad |
Citation: | Purohit, A., Pindoriya, N. M.. (2015). Socioeconomic Implications of Wind Energy – A Perspective from Gujarat. 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. Indian Institute of Management, Ahmedabad |
Series/Report no.: | IC 15;058 |
Abstract: | India possesses an abundant reserve of renewable sources of energy. These renewable energy sources may not limit to wind, solar, hydro and bio-mass. In order to harness energy from such renewable sources, a lot of policies and infrastructure has been planned and placed by the Government. However, implementation and optimal utilization of the renewable sources of energy has not been substantial as such sources are highly intermittent in nature. In this paper, we have selected wind energy, one of the most intermittent sources of energy, its trends, technical and operational factors that play a key role in generation of electricity. The study is limited to policies and wind trends of Gujarat, one of the richest sources of wind energy resources in the country. The study aims at creating a model to forecast wind energy generation to counter the variability of wind energy and minimize financial as well as operational hassles for the wind energy generator as well as the system operator. The wind profiles, wind energy produced and various institutional factors are considered. Linear regression is employed to predict the wind energy generation. Indian Electricity Grid codes, working dependencies, errors encountered during statistical modeling and respective losses to the wind energy generators are taken in consideration for a coherent understanding and analysis of implications of wind energy. |
URI: | http://hdl.handle.net/11718/14044 |
Appears in Collections: | 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence |
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