Power engineering cost prediction model based on BP neural network

As the core of power engineering investment decision, power engineering cost prediction plays a key role in optimizing resource allocation and cost control. Aiming at the problems such as local optimization and insufficient generalization ability of traditional BP neural network, this paper proposes a BP neural network prediction model optimized by thinking evolutionary algorithm (MEA), and constructs an intelligent cost prediction system suitable for power transmission and transformation projects through dynamic weight adjustment and threshold optimization mechanism. The experiment selects the data of 60 power transmission and transformation projects of a power company in 2023, and adopts the 50:10 sample division strategy for training and testing. The results show that the average error of the improved model is reduced to 3%, which is 66.7% lower than that of the traditional BP network. The average error of forecasting unit capacity cost of power transformation project is 2%, and the accuracy is improved by 83.3%. Through algorithm innovation and integration of engineering features, this paper breaks through the technical bottlenecks of traditional forecasting methods in terms of spatiotemporal heterogeneity data adaptation and emergency event response ability, and provides a solution with both theoretical value and practical efficiency for the cost management and control of the whole life cycle of power engineering.