Frequency-Based Spatial–Temporal Mixture Learning for Load Forecasting

Load forecasting plays a vital role in key areas such as energy forecasting and resource management. Traditional forecasting methods are often limited in dealing with shifts in statistical distribution and dynamic changes in periodic parameters of load data, making it difficult to capture complex temporal dependencies and periodic change patterns. To address this challenge, we transform load data into the frequency domain and use a fine-tuned large language model for forecasting. Specifically, we propose the Frequency-Based Spatial–Temporal Mixture Learning Model (FSTML), which uses (1) a Frequency-domain Global Learning Module (FGLM), (2) Temporal-Dimension Learning Module (TDLM), and (3) Spatial-Dimension Learning Module (SDLM) to process load data and extract comprehensive temporal patterns. FGLM transfers load data to the frequency domain and provides the model with a frequency-domain global feature representation of load data. The TDLM and SDLM fine tune the pre-trained large language model in the time dimension and space dimension, respectively, extracting the temporal dependency and spatial dependency of load data, respectively, thereby extracting the spatial–temporal pattern of load data. FSTML achieves the best performance in the forecasting task on two public load datasets, and the forecasting accuracy is significantly improved. The high-precision load forecasting model proposed in this study can significantly improve the operational efficiency of power systems and the integration capacity of renewable energy sources, thereby supporting the sustainable development of the power industry in three dimensions: energy optimization, emission reduction, and economic operation.