Optimized nonlinear neural network architectural models for multistep wind speed forecasting

Abstract

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.