Facing the increasingly severe external environment, it is difficult to predict the material price fluctuations accurately, which makes it more difficult to control the transmission line engineering cost. This paper first analyzes the factors affecting the price of power engineering materials, sorts out 9 influencing factors; constructs the neural network power engineering material pricing model (GA-BPNN), optimizes the weight and threshold of BPNN, and selects 35 tower material prices as the training and test samples of the model. The results show that the average absolute percentage error of GA-BPNN results is 7.55%, which is better than BPNN model.
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