Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

Alma Y. Alanis, Oscar D. Sanchez and Jesus G. Alvarez
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Alma Y. Alanis: University Center of Exact Sciences and Engineering, University of Guadalajara, Marcelino Garcia Barragan 1421, Guadalajara, Jalisco 44430, Mexico
Oscar D. Sanchez: University Center of Exact Sciences and Engineering, University of Guadalajara, Marcelino Garcia Barragan 1421, Guadalajara, Jalisco 44430, Mexico
Jesus G. Alvarez: University Center of Exact Sciences and Engineering, University of Guadalajara, Marcelino Garcia Barragan 1421, Guadalajara, Jalisco 44430, Mexico

Mathematics, 2021, vol. 9, issue 10, 1-18

Abstract: Wind energy is one of the most promising alternatives as energy sources; however, to obtain the best results, producers need to forecast the wind speed, generated power and energy price in order to provide the appropriate tools for optimal operation, planning, control and marketing both for isolated wind systems and for those that are interconnected to a main distribution network. For the present work, a novel methodology is proposed for the forecasting of time series in wind energy systems; it consists of a high-order neural network that is trained on-line by the extended Kalman filter algorithm. Unlike most modern artificial intelligence methods of forecasting, which are based on hybridizations, data pre-filtering or deep learning methods, the proposed method is based on the simplicity of implementation, low computational complexity and real-time operation to produce 15-step-ahead forecasting in a time series of wind speed, generated power and energy price. The proposed scheme has been evaluated using real data from open access repositories of wind farms. The results show that an on-line training of the neural network produces high precision, without the need for any other information beyond a few past observations.

Keywords: wind energy; energy price; artificial neural networks; renewable energy systems; time series forecasting; extended Kalman filter learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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