Data Driven Load Forecasting Method Considering Demand Response

With the continuous development of various flexible resources, smart grid big data and power demand side management, the power load characteristics have undergone great changes, which makes the traditional load forecasting method needs further improvement. A daily load forecasting method based on the data-driven concept is proposed in this paper to coping with changes in load characteristics and the processing of massive data. Firstly, the load characteristics of massive data are extracted. Then, establishing the classifier to obtain the coupling relationship between influencing factors and different characteristic loads which we can know the category of the forecast day. Using the least squares support vector machine method to establish forecasting model for specific categories. On this basis, considering the influence of user demand side response on the load curve, using the electricity price elastic matrix to correct the above forecasting result. After the analysis and comparison of specific examples, the effectiveness of the proposed method is verified.