Short-term Load forecasting method based on multi-source data fusion

Due to the complexity and diversity of power loads, this study aims to address the challenging problem of accurate power load forecasting. This paper establishes a typical load electricity consumption prediction model using big data technology, incorporating uncertain factors such as meteorological conditions and human activities to improve load prediction accuracy. Initially, the fuzzy C-means algorithm is utilized to categorize electricity users into commercial and residential segments. Following this, a multi-source data fusion algorithm is applied to forecast the electricity consumption for each user type by integrating various influencing factors. The proposed approach significantly enhances the accuracy of load forecasting.