Research on short-term load forecasting based on clustering and deep learning

Abstract

In power systems, power load forecasting is essential to ensure the reliability and efficiency of power supply. Since power load is affected by many factors, including weather, seasonality, and social activities, its patterns and changes are complex and diverse, and traditional forecasting methods may make it difficult to meet demand. In this background, this study combines the K-means clustering algorithm and deep learning model. First, through K-means clustering, we grouped historical load data, dates, and temperatures to identify different load patterns. This grouping helps adapt to different load changes, thereby improving the adaptability of the forecast. Then, a deep learning model is applied, combining a convolutional neural network (CNN) and a two-layer bidirectionally gated recurrent unit (BIGRU). These two models are used to deal with spatiotemporal characteristics and sequence dependence in load data respectively. CNN is used to capture the spatial features in the load data, while BIGRU processes the time series information in the data to effectively capture the complex dynamic nature of the load. K-means clustering information is entered as additional data into the CNN and BIGRU models. The experimental results of the study show that by fully considering different load patterns and data characteristics. Reducing load demand and providing more reliable short-term load forecasts improve the efficiency of the power supply, reduce energy waste, and reduce the burden on the power system.