首页|基于新型多目标浣熊优化算法的BiLSTM-Attention预测模型及误差分析

基于新型多目标浣熊优化算法的BiLSTM-Attention预测模型及误差分析

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工程造价预测在现代工程管理中具有重要意义.然而,受市场波动、人力成本等因素影响,工程造价预测一直具有挑战性.本文提出一种新型多目标浣熊优化算法,并提出基于该算法优化的双向长短期记忆网络(BiLSTM)和注意力机制(Attention)的变电工程造价预测模型.首先,将本文算法与主流多目标优化算法在8个测试问题上进行对比,验证多目标浣熊优化算法的有效性;其次,通过本文算法对预测模型进行优化,实现模型精度提升;通过BiLSTM-Attention模型捕捉历史数据中的潜在关系,提高变电工程造价预测的精度和可靠性;最后,将本文模型与主流的5种模型进行对比,使用某省110 kV变电工程的历史数据作为案例研究.结果显示,本文模型的平均绝对百分比误差为3.71%,相比BP减小了9.82个百分点,相比ANN减小了5.81个百分点,相比LSTM减小了5.40个百分点,相比LSTM-SVR减小2.03个百分点,相比CNN-LSTM减小1.00个百分点.
BiLSTM-Attention Prediction Model and Error Analysis Based on Novel Multi-objective Coati Optimization Algorithm
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.

engineering costmulti-objective coati optimization algorithmBiLSTMattention mechanismprediction

李钧超、尤菲、张超、苏乐乐、龚龑

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国网宁夏电力有限公司经济技术研究院,宁夏 银川 750000

宁夏回族自治区电力设计院有限公司,宁夏 银川 750000

工程造价 多目标浣熊优化算法 双向长短期记忆网络 注意力机制 预测

2024

计算机与现代化
江西省计算机学会 江西省计算技术研究所

计算机与现代化

CSTPCD
影响因子:0.472
ISSN:1006-2475
年,卷(期):2024.(11)