Residential Load Forecasting Using Modified Federated Learning Algorithm

Abstract

The feasibility of utilizing electricity consumption data is increasing with the widespread use of advanced metering infrastructure among consumers. In addition, the need for consumer demand management and services is likely to increase further as distributed resources are activated. Load forecasting technology is essential for customer demand management. Forecasting technology using deep learning has been applied extensively in recent years. Deep-learning-based load forecasting requires data for training a forecasting model, and the available data may be limited by the Personal Information Protection Act. Federated learning has emerged as a solution to this problem. However, in federated learning, the global model in the central server is trained by aggregating the local model sent from the client without filtration. This may result in the global model overfitting or failure to converge by aggregating the model that is not trained effectively in the client. In this study, we introduce a modified federated learning algorithm to solve this problem and propose a method to forecast residential loads. The proposed method is analyzed and evaluated experimentally using smart meter data. The experimental results reveal that the proposed method improves forecasting performance and convergence. For the global model, when the number of clients was two, three, four, and five, the forecasting performances were 4.891, 5.228, 5.488, and 5.633, respectively, on a mean absolute percentage error (MAPE) basis, showing performance improvements of 28.4%, 10.0%, 16.3%, and 5.8%.