Short-term load forecasting (STLF) is an essential and challenging task for power- or energy-providing companies. Recent research has demonstrated that a framework called “decomposition and ensemble” is very powerful for energy forecasting. To improve the effectiveness of STLF, this paper proposes a novel approach integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), grey wolf optimization (GWO), and multiple kernel extreme learning machine (MKELM), namely, ICEEMDAN-GWO-MKELM, for STLF, following this framework. The proposed ICEEMDAN-GWO-MKELM consists of three stages. First, the complex raw load data are decomposed into a couple of relatively simple components by ICEEMDAN. Second, MKELM is used to forecast each decomposed component individually. Specifically, we use GWO to optimize both the weight and the parameters of every single kernel in extreme learning machine to improve the forecasting ability. Finally, the results of all the components are aggregated as the final forecasting result. The extensive experiments reveal that the ICEEMDAN-GWO-MKELM can outperform several state-of-the-art forecasting approaches in terms of some evaluation criteria, showing that the ICEEMDAN-GWO-MKELM is very effective for STLF.
Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine
