A Machine Learning-Based Probabilistic Electricity Load Forecasting Method under Extreme Weather Conditions

In the energy transition of the world, the models to be used in power load prediction should be capable of delivering predictions that are not only accurate but also have a reasonable measure of uncertainty. The increase in the number of extreme weather events has caused the behavior of the loads to be nonlinear and unpredictable and this has restricted the effectiveness of the traditional deterministic forecasting approach in grid dispatching as well as warning of risk. To address pattern identification, data sparsity, and uncertainty under extreme weather, this paper develops an integrated probabilistic forecasting framework with three linked stages: extreme-weather load identification, TimeGAN-based sample augmentation, and conformal quantile forecasting. The method first builds a high-confidence extreme-weather load sample repository, then augments scarce extreme-weather sequences, and finally provides calibrated prediction intervals for short-term load forecasting. Experimental results show that the proposed method improves forecasting accuracy under extreme-weather conditions, with MAPE reduced across all five tested models after data augmentation; for example, ARIMA decreases from 12.37% to 8.65% and iTransformer decreases from 6.12% to 5.28%. The conformal quantile forecasting model also achieves 97.62% empirical coverage under the nominal 95% prediction interval, indicating improved prediction-interval reliability.