A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model

Abstract

Wind is a renewable and green energy source that is vital for sustainable human development. Wind variability implies that wind power is random, intermittent, and volatile. For the reliable, stable, and secure operation of an electrical grid incorporating wind power systems, a multi-hour ahead wind power forecasting system comprising a physics-based model, a multi-criteria decision making scheme, and two artificial intelligence models was proposed. Specifically, a Weather Research and Forecasting (WRF) model was used to produce wind speed forecasts. A technique for order of preference by similarity to ideal solution (TOPSIS) scheme was employed to construct a 5-in-1 (ensemble) WRF model relying on 1334 initial ensemble members. Two adaptive neuro-fuzzy inference system (ANFIS) models were utilised to correct the wind speed forecasts and determine a power curve model converting the improved wind speed forecasts to wind power forecasts. Moreover, three common statistics-based forecasting models were chosen as references for comparing their predictive performance with that of the proposed WRF-TOPSIS-ANFIS model. Using a set of historical wind data obtained from a wind farm in China, the WRF-TOPSIS-ANFIS model was shown to provide good wind speed and power forecasts for 30-min to 24-h time horizons. This paper demonstrates that the novel forecasting system has excellent predictive performance and is of practical relevance.