Multi-Scale Multi-Load Federated Forecasting Method with Mode Decomposition

Abstract

Accurate load forecasting is the premise of efficient and stable operation of integrated energy systems. For multiple integrated energy systems that have insufficient energy consumption data but similar energy consumption behavior, federated learning can establish a higher accuracy multi-load forecasting model for each system without disclosing data privacy. However, the existing federated learning methods cannot fully utilize common and individual characteristics in the energy consumption data of different nodes (that is, integrated energy systems), which obviously affects their prediction accuracy. In view of this, we propose a multi-time scale multi-load federated forecasting method based on mode decomposition (MD-MMFF). Firstly, a multivariate empirical mode decomposition method is introduced to decompose the energy consumption data of each node into two types, i.e., regular components and irregular components. Each node uses the local LSTM model to learn the regular components and predict their outputs. For the irregular components, a multi-load federated forecasting training mechanism based on knowledge distillation is proposed, and the corresponding multi-load forecasting model is jointly established for each node. Then, the predicted values of regular components and irregular components are integrated to obtain the final multivariate load forecasting results. Experimental results show that compared with the existing multi-load forecasting algorithms, the proposed MD-MMFF method can obtain higher accuracy multi-source load forecasting results.