A hybrid model of modal decomposition and gated recurrent units for short-term load forecasting - Chun-Hua Wang; Wei-Qin Li (PeerJ Computer Science, 2023)
Electrical load forecasting is important to ensuring power systems are operated both economically and safely. However, accurately forecasting load is difficult because of variability and frequency aliasing. To eliminate frequency aliasing, some methods set parameters that depend on experiences. The present study proposes an adaptive hybrid model of modal decomposition and gated recurrent units (GRU) to reduce frequency aliasing and series randomness. This model uses average sample entropy and mutual correlation to jointly determine the modal number in the decomposition. Random adjustment parameters were introduced to the Adam algorithm to improve training speed. To assess the applicability and accuracy of the proposed hybrid model, it was compared with some state of the art forecasting methods. The results, which were validated by actual data sets from Shaanxi province, China, show that the proposed model had a higher accuracy and better reliability compared to the other forecasting methods.