A Prediction Model for Maintenance Engineering Cost of Sub-stations Based on Machine Learning

With the increase in electricity demand, the rising maintenance costs of substation projects have put power grid companies under huge market pressure. Traditional cost methods cannot meet the needs of modern markets, so the construction of intelligent cost calculation models is the key to realize the modernization and refinement of capital management and control of power grid enterprises. In this paper, the cost composition of the primary equipment maintenance of the substation is analyzed, and the engineering parameters, equipment parameters and process parameters are determined to be the key influencing factors of the total project cost by analyzing the historical engineering settlement data. On this basis, the deep belief network was used to establish the measurement model for maintenance engineering cost prediction. Experiments are carried out on the actual engineering data of Guangdong Province, and the prediction results show that the proposed calculation method and model have good accuracy and reliability.