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Abstract
Wind energy is an important cornerstone of a non-polluting and sustainable electricity supply. In practice, the integration of significant wind energy into the existing electricity supply system is a challenge due to the stochastic nature of wind power. To be able to effectively integrate wind power into existing grid systems, accurate short-term wind speed forecasting is essential. Statistical and soft computing models are mainly used for short-term forecasting and a physical fluid model is used for long-term forecasting. Soft computing models are commonly suggested for wind forecasting due to data independency and handling of non-linear nature systems. Fuzzy ARTMAP, which is a combination of fuzzy logic and adaptive resonance theory, has been shown to be superior to basic neural network models in terms of the stability–plasticity dilemma. This paper presents a novel approach to short-term wind forecasting (i.e., 12- and 24-hr forecasting) using a Fuzzy ARTMAP technique, and the results are compared to a recently proposed linear prediction technique and a conventional neural network backpropagation algorithm.
Notes
1The fundamentals of an ANN with BP will not be presented in this paper as numerous references exist for the reader.
2Wind speed data was acquired in m/s.