Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models

Abstract

Wind energy, which is clean, inexhaustible and free, has been used to mitigate the crisis of conventional resource depletion. However, wind power is difficult to implement on a large scale because the volatility of wind hinders the prediction of steady and accurate wind power or speed values, especially for multi-step-ahead and long horizon cases. Multi-step-ahead prediction of wind speed is challenging and can be realized by the Weather Research and Forecasting Model (WRF). However, a large error in wind speed will occur due to inaccurate predictions at the beginning of the synoptic process in WRF. Multi-step wind speed predictions using statistical and machine learning methods have rarely been studied because greater numbers of forecasting steps correspond to lower accuracy.In this study, a detailed review of wind speed forecasting is presented, including the application of wind energy, time horizons for wind speed prediction and wind speed forecasting methods. This paper presents eight strategies for realizing multi-step wind speed forecasting with machine-learning methods and compares 48 different hybrid models based on these eight strategies.The results show good consistency among the different wind farms, with COMB-DIRMO models generally having a higher prediction accuracy than the other strategies. Thus, this paper introduced three methods of combining these COMB-DIRMO models, an analysis of their performance improvements over the original models and a comparison among them. Valid experimental simulations demonstrate that ALL-DDVC, one combination of the COMB-DIRMO models, is a practical, effective and robust model for multi-step-ahead wind speed forecasting.