Optimized nonlinear neural network architectural models for multistep wind speed forecasting. (September 2019)

• Record Type: Journal Article
• Authors: Begam, K. Maruliya; Deepa, S.N.
• Is Part Of: Computers & electrical engineering. Volume 78(2019)
• Page Start: 32
• Page End: 49
• Publication Date: 2019-09
• Subjects: Wind power -- Wind speed -- Multistep forecasting -- Nonlinear neural network model -- Particle swarm optimization -- Firefly algorithm -- Forecasting accuracy; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.06.018
• Languages: English
• ISSNs: 0045-7906

Abstract

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.