Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-Term Load Forecasting in Electricity Wholesale Markets

Abstract

Short-term load forecasting (STLF) plays a pivotal role in operational efficiency of power plants. Leveraging data from utility companies for STLF in a wholesale market presents challenges. Notably, data sharing reluctance from utility companies, driven by privacy considerations, limits the availability of valuable forecasting information. Concurrently, due to the growing reliance on information and communication technologies, data integrity attacks (DIAs) and communication noise are emerging as a significant concern, which is largely overlooked in existing research. We propose an innovative approach combining deep reinforcement learning (DRL) with federated learning (FL) to construct a robust STLF model that meets privacy constraints and operates efficiently. By employing FL, we facilitate collaboration between the power plant and multiple utility companies to generate a STLF model for the power plant, circumventing the need for direct access to raw data from utility companies, thereby preserving data privacy. To counteract model degradation induced by DIAs and noise in communication channels, we incorporate DRL into our methodology. Simulation outcomes affirm the efficacy of our proposed approach, demonstrating its capacity to deliver accurate and resilient STLF for power plants, even in the presence of DIAs and communication noise.