Minute-level ultra-short-term power load forecasting based on time series data features

Abstract

Abstract

Electricity is fundamental to the development of national economies and societies, reliant on accurate power load forecasting for its stable supply. Ultra-short-term power load forecasting analyzes historical power load data to predict load changes within the next hour. This forecasting is crucial for achieving efficient power dispatching, improving emergency management, and ensuring the stable operation of the power system. However, with the increasingly widespread application of renewable energy, its inherent intermittency exacerbates the complexity and randomness of power loads, posing a challenge for models to accurately capture data features. In addressing this challenge, the study presents a novel method for feature extraction from time series data, aimed at enhancing the accuracy of power load forecasting. By analyzing trend, periodicities, and randomness, it simplifies complex time series data into several stable data features, significantly reducing noise-induced errors and enhancing the identification and understanding of power data features. Moreover, this study applies the feature extraction method to five prevalent deep learning models. Experimental results show that the deep learning models using this feature extraction method reduces the mean absolute percentage error by an average of 54.6905%, 42.6654%, and 51.3868% on datasets from three different substations in China. These results not only affirm the method's efficacy in forecasting power load but also provide new technical foundations for the reliable functioning of future power systems.