A novel decomposition-ensemble forecasting system for dynamic dispatching of smart grid with sub-model selection and intelligent optimization

Abstract

Short-Term Electricity Load Forecasting (STLF) has drawn increasing attention due to its considerable function on improving the dispatch and management of power system. The path of load forecasting has been widely explored in the past studies. However, most forecasting methods ignore the importance of sub-model selection and interval forecasting, causing poor forecasting performance. In this paper, a hybrid ensemble forecasting scheme, constructed by sub-model selection, data decomposition, multi-objective optimization, point prediction, and interval prediction, is proposed to enrich the current load forecasting system. In the load forecasting system design, the original load data is adaptively decomposed into some sub-series by using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). The most proper sub-model for decomposed series is selected from nine widely-used forecasting methods based on sub-model selection. The forecasting results of each sub-series are ensembled by the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) to estimate the final point forecasting values. By integrating point forecasting errors and distribution fitting strategy, the interval forecasting results are further estimated, which investigates the effectiveness of the proposed system. Three numerical experiments are carried out based on the real load data obtained from three observation sites. The experimental results indicate that the hybrid ensemble system can provide better point and interval forecasting performance with the average Mean Absolute Percentage Error (MAPE) values of 0.3275%, 0.4419%, and 0.7024% and the mean Coverage Width-based Criteria (CWC) values (α=0.05) of 0.0668, 0.1335, and 0.3178 from one-step to three-step forecasting, respectively.