High-Precision User Load Forecasting Based On Wavelet Denoising

By grasping users' electricity consumption rules, it is possible to accurately forecast the electricity consumption, which can improve the operation efficiency of the grid and carry out a demand-side response. It is the significance of maintaining the stability of the grid. Through the analysis of user load, the characteristics of user load are found, and a personalized pattern mining prediction method based on wavelet denoising is proposed for these characteristics. This method can mine user historical load data for pattern extraction and separately perform load forecasts for users which in different power consumption patterns. The analysis of the calculation result shows that the accuracy of the proposed method is higher than ordinary prediction methods.