Wind speed forecasting based on multi-objective grey wolf optimisation algorithm, weighted information criterion, and wind energy conversion system: A case study in Eastern China - Wang, Chen; Zhang, Shenghui; Xiao, Ling; Fu, Tonglin (Energy conversion and management, 2021)
•A secondary denoising algorithm (SSA-EEMD) is proposed to extract the chief characters of wind speed series.•The combined model is based on sub model selection and multi-objective grey wolf optimisation algorithm.•The Sub Model Selection is based on weighted information criterion (WIC) and hypothesis test.•A modified multi-objective optimization algorithm is used to optimize weights and thresholds and applied in forecast.•The conversion of wind power to electrical energy was provided a feasible method in wind farms. Accurate wind speed forecasting and effective wind energy conversion can reduce the operating cost of wind farms. However, many previous studies have been restricted to analyses of wind speed forecasting and wind energy conversion, which may result in poor decisions and inaccurate power scheduling for wind farms. This study develops a wind energy decision system based on forecasting and simulation, which includes two modules: wind speed forecasting and wind energy conversion. In the wind speed forecasting module, an effective secondary denoising strategy based on singular spectrum analysis and ensemble empirical mode decomposition was used to eliminate chaotic noise and extract important features from the original data. Then, a model selection called weighted information criterion was applied to select optimal sub-models for the combined model. To improve the forecasting performance of the combined model, a modified multi-objective grey wolf optimisation algorithm was adopted to optimise the parameters of the sub-models and the weight of the combined model. In the wind energy conversion module, a wind energy conversion curve was established by simulating historical electrical energy data and wind speed data, which can effectively analyse the power generation at each site. The numerical results show that compared with the mean absolute percentage error values of the single models, that of the combined model is reduced by up to 35.57%. Moreover, the standard deviation of the absolute percentage error is decreased by up to 49.88% for wind speed forecasting, and the R2 of the wind energy conversion curve is more than 0.9. Therefore, the proposed combined method can serve as an effective tool for wind farm management and decision-making.