Electricity load forecasting by randomized neural networks - Gao, Ruobin (Nanyang Technological University, 2022)
Recently, a hot research topic has been time series forecasting via randomized neural networks and its application in the energy system. Electricity load is a representative time series from the energy system. Forecasting the electricity load accurately benefits electric power system planning for maintenance and construction. As a result, developing novel and accurate forecasting models for short-term load is significant. Among the vast energy forecasting models, a huge branch is the hybrid models with signal decomposition blocks. However, most literature does not implement the decomposition properly. A walk-forward decomposition is proposed, which overcomes the data leakage problem—such decomposition functions by processing the observations in the moving window. The latest observations are fed into the machine learning model for forecasting purposes. We adopt the random vector functional link (RVFL) network, whose hidden features are randomly initialized and fixed during training. Finally, the proposed model consists of two blocks, the walk-forward decomposition and the RVFL.