Abstract
In recent years, many countries have established their ambitious renewable energy targets to satisfy their future electricity demand with the main aim to foster sustainable and low-emission development. In meeting these targets, the changes to power system planning and operations involve the significant consideration of renewable energy generation through mainly wind energy and solar energy which are more variable and uncertain as compared to the conventional sources (i.e. Thermal and Nuclear Energy). In the present paper, three machine learning methods named as Support Vector Machine, Artificial Neural Network and Multiple Linear Regression are applied to forecast wind power generation on basis of past data of wind direction and wind speed. The impact of input variables such as wind speed and wind direction on wind power generation is investigated and compared.
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Gupta, Y., Saraswat, A. (2021). Machine Learning Techniques for Short-Term Forecasting of Wind Power Generation. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in Intelligent Systems and Computing, vol 1141. Springer, Singapore. https://doi.org/10.1007/978-981-15-3383-9_39
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DOI: https://doi.org/10.1007/978-981-15-3383-9_39
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