Optimizing Electric Vehicle Charging Load Forecasting via Ensemble of Diffusion Models

The global adoption of electric vehicles (EVs) has experienced rapid growth in recent years. This has resulted in higher EV charging demands, placing additional pressure and challenges on existing smart grid infrastructure. Accurate and reliable short-term electric vehicle load predictions are essential to optimize smart grid operations, prevent overload of the grid and transformers, and ensure that electricity distribution aligns with actual demand. Although machine learning methods have found wide use in electric vehicle charging load forecasting, they struggle to capture complex feature patterns and accurately quantify uncertainties. In contrast, generative AI has shown great promise in various real-world applications due to its ability to model intricate data distributions and provide robust probabilistic forecasts. In this work, we leverage denoising diffusion probabilistic models, a type of generative model, to address the challenges of probabilistic electric vehicle load forecasting. Specifically, we propose an ensemble of conditional diffusion models as a unified framework to improve forecasting accuracy. We evaluate our approach on several real-world datasets across multiple forecasting horizons. The results demonstrate that the proposed method consistently improves forecasting accuracy compared with frequently used forecasting methods.