Electricity Consumption Forecasting Using Extreme Learning Machine With The Generalized Maximum Correntropy

The forecasting technology of electricity consumption is essential both in power system scheduling and in the open power market. However, in the current electricity sales company's electricity consumption forecasting problems, the historical electricity data and the error distribution of electricity consumption forecasting are usually nonlinear and non-Gaussian. Due to the small user scale, some random events and data collection errors will cause outliers. These outliers has a greater impact on the prediction results, which makes traditional prediction methods no longer applicable. In order to solve this problem, this paper uses generalized maximum correntropy criterion as a new loss function to replace the mean squared error loss function in the traditional ELM, and constructs the GMCCELM electricity prediction model. Finally, the historical electricity consumption data of a commercial user in Guangdong province and the corresponding temperature data are applied to the electricity consumption prediction model established in this paper. An actual case is given to illustrate the superiority and effectiveness of the electricity consumption prediction model compared with other existing methods. The prediction results show that, compared with the traditional ELM and other algorithms, the robust prediction model established in this paper can effectively improve the accuracy of electricity consumption forecasting.