Federated Learning vs Edge Learning for Hot Water Demand Forecasting in Distributed Electric Water Heaters for Demand Side Flexibility Aggregation

Abstract

Hot water demand forecasting is crucial for estimating aggregated flexibility from distributed electric water heaters (EWHs). High-performance deep learning models for forecasting require large amounts of training data that may not be available to aggregators. Privacy preserving edge learning methods for individual EWHs cannot learn from other EWHs. To address these challenges, this paper proposes a federated learning (FL) framework for hot water demand class forecasting across multiple distributed EWHs with limited demand data. Feed-forward neural network (FNN) based global model is collaboratively trained by multiple edge devices without sharing data to predict hot water demand. The global model is specifically further fine-tuned to individual EWHs using their own data. The proposed approach is tested with synthetic hot water demand data from 40 households and the results indicate that the proposed FL framework can perform better than edge learning and has numerous other advantages.