Research and Development of Line Loss Management and Load Forecasting System for Electric Power Enterprises Based on New Energy Consumption Technology Optimization

Wind power and photovoltaic power generation have the advantages of economy and environmental protection, and have been developed rapidly at home and abroad. With the increasing proportion of new energy access to the power grid and the decreasing proportion of conventional power access, the new energy obstruction problem is highlighted. In this paper, we first analyze the high proportion of new energy consumption obstruction problem, and then establish a load forecasting model based on LSTNet, and use FedAvg algorithm and FedML library to establish a federated learning-based privacy training framework for sub-industry. Then, a method for electric-hydrogen hybrid energy storage to participate in the low-carbon and economic dispatch of a new power system is proposed. On this basis, this paper successfully designs a power enterprise line loss management and load forecasting system based on the optimization of new energy consumption technology. The global model load forecasting performs better with learning rate α = 1 × 10⁻⁴ for three groups of electricity users: industrial, commercial, and residential. Although the global load forecasting model is larger than the individually trained local model in all metrics, the performance of the global model is very close to that of the local model when combined with the actual forecasting results of the global model. After the new energy is involved in the system standby optimization, the total system cost is significantly reduced, the total system cost of mode 1 is reduced by a total of $6,383.67 compared to mode 2, and the actual power generation of wind power and PV power generation is increased by a total of 892.29 MW-h, which indicates that the reasonable inclusion of new energy into the system standby can improve the level of new energy consumption, and the overall increase in the overall comprehensive benefits of the system. Benefits.