Research on load capacity forecasting and early warning methods for power customer service

In recent years, some regions in China have been frequently hit by natural disasters, causing frequent failures of power equipment and large-scale power outages, revealing the shortcomings of the current power system in terms of carrying capacity. Based on this, in order to more accurately assess carrying capacity and strengthen disaster emergency response mechanisms, effectively alleviate the work pressure of grassroots units, this study proposes to research an innovative method for predicting and warning customer service carrying capacity. Utilizing the OGG technology to collect impact data, setting different carrying capacity thresholds through on-site surveys; at the same time, utilizing big data technology and deep belief network to build a customer service carrying capacity prediction model, predicting personnel and material carrying capacity; finally, establishing a regional hierarchical warning mechanism to achieve precise classification, effective identification, and warning of regional customer service carrying capacity. The research results have important implications for enhancing the agility of emergency response, reducing losses caused by power outages, and optimizing the service efficiency of the power system.