Power Demand Response Prediction and Optimization Strategy Based on Knowledge Graph

With the development of the economy, the demand for electricity continues to increase, and the scale of the power system continues to expand. At the same time, the supply-demand contradiction is becoming increasingly prominent. Affected by pressure such as emission reduction, the expansion of the power generation industry is greatly restricted. Therefore, relying solely on scheduling resources on the power generation side can no longer meet the situation of energy shortage and power shortage. Against the backdrop of increasing electricity demand from residential users, it is particularly important to study the electricity consumption behavior of end-users in the power grid. This article comprehensively considers the factors that affect user electricity demand and studies the response mechanism of electricity demand with changes in influencing factors. This article comprehensively considers the factors that affect user electricity demand, studies the electricity demand response mechanism, and introduces a knowledge graph-based method for predicting the scale of electricity demand response and subsidy amount. By integrating multidimensional data such as user electricity consumption behavior, equipment operation status, and environmental factors, a multi-dimensional correlation relationship and knowledge model are constructed to achieve accurate prediction of future electricity demand response scale and subsidy amount. The experimental results show that on January 15, 2024, user U001 participated in peak shaving response, reducing electricity by 50 kWh and receiving a subsidy of 42.5 yuan; User U002 participated in the valley adjustment response, reducing electricity by 4 kWh and receiving a subsidy of 2.2 yuan. It is expected that by 2025, the demand response scale in Shanghai will reach 400000 kilowatts, and the subsidy demand will reach 20 million yuan. This method can effectively improve the operational efficiency and reliability of the power system, while optimizing the allocation of subsidies to users and encouraging more users to actively participate in demand response.