Research on Industrial Enterprise Electricity Load Forecasting Model Based on Feature Set Construction
Obtaining user electricity consumption in advance helps maintain the reliability of the power grid and formulate scheduling strategies,therefore establishing an accurate electricity load forecasting model is of great significance. This paper establishes a method for industrial enterprise electricity load forecasting based on dataset construction. In the feature dimension,clustering algorithms are introduced to effectively classify the collected data,and correlation analysis methods are used to complete feature screening in each classification. In temporal dimension,calculate the similarity judgment index based on cosine distance,select historical data with similar features to the predicted day to form historical similar daily electricity consumption,and use it as the feature input for the prediction model. Compared with the basic prediction model,using the reconstructed feature set can reduce the error during the prediction period by 1.9％ to 5.1％.
万方数据
维普
