Abstract
As a dominant form of renewable energy sources with significant technical progress over the past decades, wind power is increasingly integrated into power grids. Wind is chaotic, random and irregular. For proper planning and operation of power systems with high wind power penetration, accurate wind speed forecasting is essential. In this paper, a novel hybrid Neural Network (NN)-based day-ahead (24 hour horizon) wind speed forecasting is proposed, where five hybrid neural network algorithms are evaluated. The five algorithms include Wavelet Neural Network (WNN) trained by Improved Clonal Selection Algorithm (ICSA), WNN trained by Particle Swarm Optimization (PSO), Extreme Learning Machine (ELM)-based neural network, Radial Basis Function (RBF) neural network, and Multi-Layer Perceptron (MLP) Neural Network. Single- and multi-features and their effect on the accuracy of wind speed prediction are also analyzed. The wind speed dataset used in this paper is Saskatchewan’s recorded historical wind speed data. Despite the excellent wind power potential, only 6.5% of the total electricity demand is currently supplied by wind power in Saskatchewan, Canada. This study paves the way for economical operation, planning, and optimization of Saskatchewan’s future wind power generation.
Highlights
Wind power plays an increasing role in the modern mixed energy landscape by producing sustainable clean energy and reducing fossil fuel-based conventional power generation
Five hybrid neural network methods (WNN trained by Improved Clonal Selection Algorithm (ICSA); Wavelet Neural Network (WNN) trained by Particle Swarm Optimization (PSO); Extreme Learning Machine (ELM)-based neural network; Radial Basis Function (RBF) neural network; and Multi-Layer Perceptron (MLP) neural network) are evaluated using measured historical wind speed data in Saskatchewan, Canada
INFLUENCE OF THE NUMBER OF FEATURES In this study, five different hybrid Neural Network (NN) techniques (WNN trained by ICSA, WNN trained by PSO, ELM, RBF, and MLP) are utilized to forecast Saskatchewan’s day-ahead wind speeds
Summary
Wind power plays an increasing role in the modern mixed energy landscape by producing sustainable clean energy and reducing fossil fuel-based conventional power generation. In Saskatchewan, the currently installed wind power capacity only approximately provides 6.5% of the electricity demand, and the province has the plan to boost wind power up to 40% of the demand by 2030 [1]. The accurate wind speed prediction methods are urgently needed to improve economical, secure, and reliable operation of wind power systems [2]. The wind speed prediction can be performed in five time scales [3]–[5]: very short-term prediction (a few seconds to 30 minutes ahead); short-term prediction (30 minutes to 6 hours ahead); medium-term prediction (6 hours to 24 hours ahead); long-term prediction (24 hours to one week ahead); and very long-term prediction (one week and longer) [6]. The applications of each time-scale are provided in [3]–[5]. To realize wind speed forecasting, the following four types of methods can be used: physical or weather-based methods; statistical or time-series-based methods; Artificial Intelligence (AI)-based methods; and hybrid methods [4], [5], [7]
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