Short-term wind speed forecasting using artificial neural networks for Tehran, Iran

Farivar Fazelpour,Marc A. Rosen,Negar Tarashkar

doi:10.1007/s40095-016-0220-6

Abstract

Wind energy is increasingly being utilized globally, in part as it is a renewable and environmental-friendly energy source. The uncertainty caused by the discontinuous nature of wind energy affects the power grid. Hence, forecasting wind behavior (e.g., wind speed) is important for energy managers and electricity traders, to overcome the risks of unpredictability when using wind energy. Forecasted wind values can be utilized in various applications, such as evaluating wind energy potential, designing wind farms, performing wind turbine predictive control, and wind power planning. In this study, four methods of forecasting using artificial intelligence (artificial neural networks with radial basis function, adaptive neuro-fuzzy inference system, artificial neural network-genetic algorithm hybrid and artificial neural network-particle swarm optimization) are utilized to accurately forecast short-term wind speed data for Tehran, Iran. A large set of wind speed data measured at 1-h intervals, provided by the Iran Renewable Energy Organization (SUNA), is utilized as input in algorithm development. Comparisons of statistical indices for both predicted and actual test data indicate that the artificial neural network-particle swarm optimization hybrid model with the lowest root mean square error and mean square error values outperforms other methods. Nonetheless, all of the models can be used to predict wind speed with reasonable accuracy.

Highlights

In recent decades, worldwide reductions of conventional energy reserves and rapidly growing energy demand have become serious public concerns
Forecasted wind values can be utilized in various applications, such as evaluating wind energy potential, designing wind farms, performing wind turbine predictive control, and wind power planning
Comparisons of statistical indices for both predicted and actual test data indicate that the artificial neural networkparticle swarm optimization hybrid model with the lowest root mean square error and mean square error values outperforms other methods

Summary

Introduction

Worldwide reductions of conventional energy reserves and rapidly growing energy demand have become serious public concerns. The use of non-renewable energy sources like fossil fuels has led to environmental concerns such as air pollution, global warming, and ozone depletion [1, 2]. To address these concerns, significant efforts have been expended to find ways to meet growing energy demands while addressing environmental concerns, leading to renewable energy sources attracting much attention globally [2]. The capacity of wind energy systems increased about 24 % in 2009 and total worldwide capacity by the end of that year reached approximately to 198 GW. In 2010, the total installed wind generation capacity increased in over 50 countries, and wind power achieved commercial use in 83 countries

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