Journal
COMPUTERS & ELECTRICAL ENGINEERING
Volume 78, Issue -, Pages 32-49Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2019.06.018
Keywords
Wind power; Wind speed; Multistep forecasting; Nonlinear neural network model; Particle swarm optimization; Firefly algorithm; Forecasting accuracy
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There is a growing demand for power from day to day. At present, the development achieved in power production from wind is highly significant. In this work, an optimized nonlinear neural network architectural model integrated with a modified firefly algorithm and particle swarm optimization is proposed to perform multistep wind-speed forecasting for specific target sites. Considering these aspects, this paper intends to predict wind speed, as its influence is high in generating wind power. The weights and bias values of the nonlinear neural network model are optimized employing the proposed optimization algorithm in order to achieve the minimum-error criterion. The computed results establish the effectiveness of the forecasting accuracy and the minimal error rate in comparison with existing methods available in the literature. (C) 2019 Elsevier Ltd. All rights reserved.
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