煤气与热力2024，Vol.44Issue(12)：15-20,27,7.
基于改进的CNN-BiGRU供热量预测方法
Heat Supply Prediction Method Based on Improved CNN-BiGRU
摘要
Abstract
An improved CNN-BiGRU heat sup-ply prediction model that integrates Dropout mechanism and Bayesian optimization is proposed.The Dropout mechanism is used to reduce the complexity of the pre-diction model and reduce the risk of the neural network falling into the local optimum.The Bayesian optimiza-tion algorithm is used to optimize the hyper-parameters of the prediction model and improve the prediction ac-curacy of the prediction model.Combined with an ex-ample,the prediction effect of the improved CNN-BiG-RU heat supply prediction model and the influence of the proportion of training set samples on the prediction effect are analyzed.The predicted values of the im-proved CNN-LSTM,CNN-GRU and CNN-BiLSTM pre-diction models significantly deviate from the measured values,while the predicted value of the improved CNN-BiGRU prediction model is closer to the measured val-ue.The improved CNN-BiGRU prediction model is better than other prediction models in all evaluation in-dexes,and the training time is relatively ideal.All things considered,the improved CNN-BiGRU predic-tion model has the best prediction effect.The predic-tion effect improves with the increase of the proportion of training set samples.When the proportion of training set samples reaches 70％,continuingly increasing the proportion of training set samples does not have much help in improving prediction performance.In order to save time and cost,it is recommended to have a train-ing set sample ratio of 70％.关键词
供热量预测/双向门控循环神经网络/卷积神经网络/Dropout机制/贝叶斯优化Key words
heat supply prediction/bidirection-al gated recurrent neural network/convolutional neural network/Dropout mechanism/Bayesian optimization分类
信息技术与安全科学引用本文复制引用
张珂,曹姗姗,孙春华,夏国强,吴向东..基于改进的CNN-BiGRU供热量预测方法[J].煤气与热力,2024,44(12):15-20,27,7.基金项目
国家重点研发计划重点专项"市政基础设施节能低碳关键技术联合研究与应用"(2022YFE0208800) （2022YFE0208800）
