Short-term residential load forecasting via transfer learning and multi-attention fusion for EVs’ coordinated charging

Abstract

Accurate load forecasting plays a crucial role in the optimal scheduling of electric vehicles’ (EVs) coordinated charging. Although many load forecasting methods have emerged in recent years, these methods face two significant challenges: effectively capturing the impact of special events on load and requiring a substantial amount of historical data for model training. To better apply day-ahead load forecasting (DALF) to the optimal scheduling of EVs’ coordinated charging, we propose a Transformer-based network architecture combining transfer learning and multi-attention fusion. The core idea of this algorithm comprehensively considers the dependencies between special events, seasonality, and load through multi-attention fusion. Simultaneously, by introducing time series decomposition block (TSDBlock), the load data is decomposed into seasonal and trend components to effectively extract crucial information, enhancing the performance of load forecasting models. To address data scarcity issues, we introduce transfer learning, selecting the best-performing model on the target task as the source model to avoid negative transfer effects. Ultimately, the experimental results show that our proposed method achieves the best forecasting performance in the datasets in five different regions. Especially in the non-working days, its performance is outstanding.

Keywords

• Day-ahead load forecasting
• Transformer
• Multi-attention fusion
• Time series decomposition
• Transfer learning
• Coordinated charging