Residential load forecasting based on electricity consumption pattern clustering

In order to reduce the peak load on the power grid, various types of demand response (DR) programs have been developed rapidly, and an increasing number of residents have participated in the DR. The change in residential electricity consumption behavior increases the randomness of electricity load power, which makes residential load forecasting relatively difficult. Aiming at increasing the accuracy of residential load forecasting, an innovative electricity consumption pattern clustering is implemented in this paper. Six categories of residential load are clustered considering the power consumption characteristics of high-energy-consuming equipment, using the entropy method and criteria importance though intercrieria correlation (CRITIC) method. Next, based on the clustering results, the residential load is predicted by the fully-connected deep neural network (FDNN). Compared with the prediction result without clustering, the method proposed in this paper improves the accuracy of the prediction by 5.21%, which is demonstrated in the simulation.