基于贝叶斯优化的BiLSTM-Adaboost热电厂热负荷预测研究

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关键词：供热负荷预测；BiLSTM-Adaboost神经网络预测；贝叶斯优化算法;超参数寻优；预测精度中图分类号：TM621.4 文献标志码：A 文章编号：2095-2945（2025）20-0001-06

Abstract：Inthispaper，aBiLSTM-AdaboostpredictionmodelbasedonBayesianoptimizationisproposedfortheheatload predictionproblemofthermalpowerplants.First，theefectsofprimarynetworkheatingparametersandmeteorologicalfactorson heatloadareconsideredcomprehensively，andthePearsoncorrelationcoeficientmethodisutilizedtoscrenthemodelinput variables.Secondly，usingthefeatureextractionabiltyofbidirectionallongshort-termmemorynetwork（BiLST）fortieeries data，AdaboostalgorithmisintroducedtointegratemultipleBiLSTMmodelstoimprovetheaccuracyandrobustnessofthe prediction;finaly，Bayesianoptimizationmethodisadoptedtooptimizethehyper-parametersofthemodeltosolvetheproblem ofreducedpredictionaccuracyduetotheperceivedimpropersetings.Simulationexperimentsarecarredoutwiththeactual operationdataofathermalpowerplant in China，andtheresultsshowthattheproposedBayesianoptimizationBiLSTMAdaboost modelhas high predictionaccuracyand stabilityin heatload predictioncomparedwith other network models.

Keywords： heating loadprediction; BiLSTM-Adaboostneural networkprediction;Bayesianoptimizationalgorithm; hyperparameter optimization;prediction accuracy

准确的供热负荷预测对保障冬季供暖、增强调峰能力、节能减排、经济运行具有重要意义

传统供热负荷预测对大量历史数据进行拟合计算，然而数学方法无法应对非线性情况。（剩余7056字）