Wind speed forecast based on combined theory, multi-objective optimisation, and sub-model selection

Abstract

Wind energy is the primary energy source for a sustainable and pollution-free global power supply. However, because of its characteristic irregularity, nonlinearity, non-stationarity, randomness, and intermittency, previous studies have only focused on stability or accuracy, and the forecast performances of their models were poor. Moreover, in previous research, the selection of sub-models used for the combined model was not considered, which weakened the generalisability. Therefore, to further improve the forecast accuracy and stability of the wind speed forecasting model, and to solve the problem of sub-model selection in the combined model, this study developed a wind speed forecasting model using data preprocessing, a multi-objective optimisation algorithm, and sub-model selection for the combined model. Simulation experiments showed that our combined model not only improved the forecasting accuracy and stability but also chose different sub-models and different weights of the combined model for different data; this improved the model generalisability. Speci cally, the MAPEs of our model are less than 4.96%, 4.60% and 5.25% in one, two and three step forecast.Thus, the proposed combined model is demonstrated as an effective tool for grid dispatching.

Multi-step prediction of carbon emissions based on a secondary decomposition framework coupled with stacking ensemble strategy

“… Sub-model selection method is crucial for combined forecasting. For different datasets, not all submodels can play an active role in combined prediction due to different data characteristics (Fu and Zhang 2022 ). Sub-model selection can select appropriate models from the candidate models of that dataset and effectively improve forecasting accuracy.…”