Federated Learning-Based Multi-Energy Load Forecasting Method Using CNN-Attention-LSTM Model
Ge Zhang, Songyang Zhu, Xiaoqing Bai
Abstract
Integrated Energy Microgrid (IEM) has emerged as a critical energy utilization mechanism for alleviating environmental and economic pressures. As a part of demand-side energy prediction, multi-energy load forecasting is a vital precondition for the planning and operation scheduling of IEM. In order to increase data diversity and improve model generalization while protecting data privacy, this paper proposes a method that uses the CNN-Attention-LSTM model based on federated learning to forecast the multi-energy load of IEMs. CNN-Attention-LSTM is the global model for extracting features. Federated learning (FL) helps IEMs to train a forecasting model in a distributed manner without sharing local data. This paper examines the individual, central, and federated models with four federated learning strategies (FedAvg, FedAdagrad, FedYogi, and FedAdam). Moreover, considering that FL uses communication technology, the impact of false data injection attacks (FDIA) is also investigated. The results show that federated models can achieve an accuracy comparable to the central model while having a higher precision than individual models, and FedAdagrad has the best prediction performance. Furthermore, FedAdagrad can maintain stability when attacked by false data injection.