首页|集中供热热力站短期热负荷预测模型对比研究

集中供热热力站短期热负荷预测模型对比研究

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以湖北省十堰市一个集中供热热力站为对象,基于实测运行数据和气象数据进行供热负荷预测研究.分别采用随机森林(Random Forest,RF)、极度梯度提升(eXtreme Gradient Boosting,XGBoost)、BP 神经网络、支持向量回归(Support Vector Regression,SVR)、长短期记忆(Long Short Term Memory,LSTM)神经网络 5 种方法进行预测模型训练及测试,基于粒子群优化算法(Particle Swarm Optimization,PSO)优化各模型参数,获得最优模型,在此基础上针对不同模型在不同短期负荷预测情景下的表现进行对比研究.研究结果表明:在未来 24h预测情景下,随机森林、XGBoost模型的预测精度最高,二者的平均绝对误差(MAE)分别为 0.84 W/m2 及 1.00 W/m2.在未来 1h预测情景下,SVR模型的预测精度最高,其MAE为 0.18 W/m2.
Comparison study of short-term heating load predicting models for district heating stations
Short-term heating load prediction is studied based on the measured operation data and meteorological data of a centralized heating station in Shiyan,Hubei Province.Five methods of random forest(RF),extreme gradient boosting(XGBoost),BP neural network,support vector regression(SVR),and long short term memory(LSTM)neural network are used for model training and testing respectively.Based on particle swarm optimization algorithm(PSO),the hyper-parameters of each predicting models are optimized,and the optimal models are obtained.On this basis,a comparative study on the performance of different models in different short-term load prediction scenarios is carried out.The research results show that:in the future 24-hour prediction scenario,the prediction accuracy of random forest and XGBoost models is the highest,and their mean absolute errors(MAE)are 0.84 W/m2 and 1.00 W/m2 respectively.In the future 1-hour prediction scenario,the prediction accuracy of SVR model is the highest,with a MAE of 0.18 W/m2.

district heatingload predictionrandom forestXGBoostBP neural networksupport vector regressionLSTM

果泽泉、何波、何强、周继平、蒋雅玲、张凡、陈超、郭放、鄢烈详

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京能东风(十堰)能源发展有限公司,湖北 十堰 442000

汉谷云智(武汉)科技有限公司,湖北 武汉 430000

集中供热 负荷预测 随机森林 极度梯度提升 BP神经网络 支持向量回归 长短期记忆神经网络

国家重点研发计划

2022YFC3802400

2024

区域供热
中国城镇供热协会

区域供热

影响因子:0.433
ISSN:1005-2453
年,卷(期):2024.(1)
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