A Method for Short-Term Electricity Load Forecasting Under Extreme Weather Conditions Based on Adaptive Signal Decomposition

The effects of long heat waves are cumulative and the electricity demand is highly non-stationary in its nature. In order to solve this issue, a multi-stage prediction system has been suggested in this paper with a focus on the extended high-temperature conditions. To begin with, scenario subsets were built by classifying historical daily load curves based on their similarity in shape. Second, fuzzy inference was used to incorporate temperature of forecast day, number of consecutive days of high temperatures and accumulated intensity of heat to create an equivalent load-response temperature feature. Third, the frequency sequence of the load is divided into two parts of high-frequency and low-frequency as well as the noise and mixing of modes are minimized. The final model of the components is then predicted separately and combined to form the resulting load prediction. The proposed framework can be evaluated using 15-minute load and meteorological data, and the results indicate that it has less MAPE, MAE, and RMSE than the comparison models and also follows the peak and rapid changes in the load during the period of prolonged heatwaves. The findings show that multiscale forecasting, load-scenario classification, and cumulative heat response representation may enhance the accuracy and strength of short-term load forecasts under the condition of sustained high-temperature levels.