Personalized Federated DARTS for Electricity Load Forecasting of Individual Buildings

Abstract

Building-level load forecasting is becoming increasingly crucial since it forms the foundation for better building energy management, which will lower energy consumption and reduce CO2 emissions. However, building-level load forecasting faces the challenges of high load volatility and heterogeneous consumption behaviors. Simple regression models may fail to fit the complex load curves, whereas sophisticated models are prone to overfitting due to the limited data of an individual building. To this end, we develop a novel forecasting model that integrates federated learning (FL), the differentiable architecture search (DARTS) technique, and a two-stage personalization approach. Specifically, buildings are first grouped according to the model architectures, and for each building cluster, a global model is designed and trained in a federated manner. Then, a local fine-tuning approach is used to adapt the cluster global model to each individual building. In this way, data resources from multiple buildings can be utilized to construct high-performance forecasting models while protecting each building’s data privacy. Furthermore, personalized models with specific architectures can be trained for heterogeneous buildings. Extensive experiments on a publicly available dataset are conducted to validate the superiority of the proposed method.