Improving the Accuracy of Load Forecasting for Campus Buildings Based on Federated Learning

Abstract

Load forecasting is important to the efficiency and reliability of the energy management systems in buildings. In general, the more data users have, the greater performance of load forecasting will be. However, collecting sufficient data for load forecasting takes a lot of time which can hardly be tolerated by users. To solve this problem, in this paper, we propose to derive the load forecasting model based on the Federated Learning for the building which has small and private data. The data are collected from the campus energy conservation supervision platform in Fuzhou University. Then the linear regression is used to study the best set of features for each building. The experimental results show that federated learning can improve the accuracy of load forecasting, while the privacy of each building is guaranteed.