Project cost prediction plays an important role in modern project management.However,due to market fluctuations,labor costs,and other factors,project cost forecasting has been challenging.Therefore,a novel multi-objective coati optimiza-tion algorithm is proposed,and a bidirectional long short-term memory network(BiLSTM)and attention mechanism optimized based on this algorithm are proposed to predict the cost of substation engineering.Firstly,the proposed algorithm is compared with the mainstream multi-objective optimization algorithm on 8 test problems,and the effectiveness of the multi-objective coati optimization algorithm is verified.Secondly,the proposed algorithm is used to optimize the prediction model to improve the accu-racy of the model.The BiLSTM-Attention model captures the potential relationship in historical data to improve the accuracy and reliability of power transformation project cost prediction.Finally,the proposed model is compared with the five mainstream mod-els,and the historical data of a 110 kV power transformation project in a province is used as a case study.The results show that the average absolute percentage error of the proposed model is 3.71%,which is reduced by 9.82 percentage points compared with BP,5.81 percentage points compared with ANN,5.40 percentage points compared with LSTM,2.03 percentage points compared with LSTM-SVR,and 1.00 percentage points compared with CNN-LSTM.
维普
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