Wind Power Forecasting Models: Classification based on Type of Model, Time Horizon and Inputs, Error Metrics and Applications

Abstract

Across the globe, renewable generation integration has been increasing in the last decades to meet ever-increasing power demand and emission targets. Wind power has dominated among various renewable sources due to the widespread availability and advanced low-cost technologies. However, the stochastic nature of wind power results in power system reliability and security issues. This is because the uncertain variability of wind power results in challenges to various system operations such as unit commitment and economic dispatch. Such problems can be addressed by accurate wind power forecasts to great extent. This attracted wider investigations for obtaining accurate power forecasts using various forecasting models such as time series, machine learning, probabilistic, and hybrid. These models use different types of inputs and obtain forecasts in different time horizons, and have different applications. Also, different investigations represent forecasting performance using different performance metrics. Limited classification reviews are available for these areas and detailed classification on these areas will help researchers and system operators to develop new accurate forecasting models. Therefore, this paper proposes a detailed review of those areas. It concludes that even though quantum investigations are available, wind power forecasting accuracy improvement is an ever-existing research problem. Also, forecasting performance indication in financial term such as deviation charges can be used to represent the economic impact of forecasting accuracy improvement.