基于数据挖掘的冷水机组能耗预测
|
摘要
为了充分利用能源站冷水机组实际运行数据,提高能耗预测准确率,提出了一种基于数据挖掘算法的冷水机组能耗预测模型。该模型包含3个主要步骤:数据预处理、模型建立及分析、结果表述。在模型选择上,利用支持向量机、径向基函数神经网络及决策树3种算法建模并对比分析。结果表明:基于数据挖掘的能耗预测模型有较好的实用性与可靠性;相比其他2种模型,径向基函数神经网络模型的均方根误差值平均降低了0.661,相关系数达到0.999,即径向基函数神经网络的能耗预测准确率最高,建模效果最佳。
In order to make full use of the actual operation data of chillers in energy station to improve the accuracy of energy consumption prediction, presents an energy consumption prediction model based on data mining algorithm. The model consists of three main steps: data preprocessing, modeling and analysis, and result presentation. Selects three kinds of algorithms of support vector machine, radial basis function neural network and decision tree for modeling and comparison. The results show that the energy consumption prediction model based on data mining has good practicability and reliability. Compared with the other two models, the root mean square error of the radial basis function neural network model is reduced by 0.661 and the correlation coefficient is 0.999. The radial basis function neural network has the highest accuracy of energy consumption prediction and the best modeling effect.
In order to make full use of the actual operation data of chillers in energy station to improve the accuracy of energy consumption prediction, presents an energy consumption prediction model based on data mining algorithm. The model consists of three main steps: data preprocessing, modeling and analysis, and result presentation. Selects three kinds of algorithms of support vector machine, radial basis function neural network and decision tree for modeling and comparison. The results show that the energy consumption prediction model based on data mining has good practicability and reliability. Compared with the other two models, the root mean square error of the radial basis function neural network model is reduced by 0.661 and the correlation coefficient is 0.999. The radial basis function neural network has the highest accuracy of energy consumption prediction and the best modeling effect.
引文
[1] 张朝晖,陈敬良,刘晓红,等. 关于制冷空调行业转型升级发展的思考[J]. 制冷与空调,2013,13(1):1- 5
[2] FAN C, XIAO F, WANG S. Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques[J]. Applied Energy, 2014, 127: 1- 10
[3] YALCINTAS M. An energy benchmarking model based on artificial neural network method with a case example for tropical climates[J]. International Journal of Energy Research, 2006, 30(14):1158- 1174
[4] YALCINTAS M. An energy benchmarking model based on artificial neural network method utilizing US Commercial Buildings Energy Consumption Survey (CBECS) database[J]. International Journal of Energy Research, 2007, 31(4): 412- 421
[5] TSO G K F, YAU K K W. Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks[J]. Energy,2007, 32(9): 1761- 1768
[6] 江亿. 我国建筑能耗趋势与节能重点[J]. 建设科技, 2006, 5(7): 10- 15
[7] 李明海,赵明强,刘敏,等. 基于GM-BP神经网络的校园建筑能耗预测[J]. 建筑节能,2016,44(11):80- 83
[8] 周芮锦,潘毅群,黄治钟. 基于时间序列分析的建筑能耗预测方法[J]. 暖通空调, 2013,43(8): 71- 76
[9] SAIDUR R, HASANUZZAMAN M, MAHLIA T M I, et al. Chillers energy consumption, energy savings and emission analysis in an institutional buildings[J]. Energy, 2011, 36(8): 5233- 5238
[2] FAN C, XIAO F, WANG S. Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques[J]. Applied Energy, 2014, 127: 1- 10
[3] YALCINTAS M. An energy benchmarking model based on artificial neural network method with a case example for tropical climates[J]. International Journal of Energy Research, 2006, 30(14):1158- 1174
[4] YALCINTAS M. An energy benchmarking model based on artificial neural network method utilizing US Commercial Buildings Energy Consumption Survey (CBECS) database[J]. International Journal of Energy Research, 2007, 31(4): 412- 421
[5] TSO G K F, YAU K K W. Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks[J]. Energy,2007, 32(9): 1761- 1768
[6] 江亿. 我国建筑能耗趋势与节能重点[J]. 建设科技, 2006, 5(7): 10- 15
[7] 李明海,赵明强,刘敏,等. 基于GM-BP神经网络的校园建筑能耗预测[J]. 建筑节能,2016,44(11):80- 83
[8] 周芮锦,潘毅群,黄治钟. 基于时间序列分析的建筑能耗预测方法[J]. 暖通空调, 2013,43(8): 71- 76
[9] SAIDUR R, HASANUZZAMAN M, MAHLIA T M I, et al. Chillers energy consumption, energy savings and emission analysis in an institutional buildings[J]. Energy, 2011, 36(8): 5233- 5238