Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network
As the energy consumption of residential building takes a large part in the building energy consumption, it is important to promote energy efficiency in residential building for green development. In order to evaluate the energy consumption of residential building more effectively, this paper propos...
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Hindawi-Wiley
2021-01-01
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Series: | Geofluids |
Online Access: | http://dx.doi.org/10.1155/2021/7271383 |
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doaj-9ff8a1eebbc54d51b2e54d09e2585c402021-10-04T01:59:22ZengHindawi-WileyGeofluids1468-81232021-01-01202110.1155/2021/7271383Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural NetworkXuenan Zhang0Jinxin Zhang1Jinhua Zhang2YuChuan Zhang3Business SchoolBusiness SchoolSchool of Economics and ManagementBusiness SchoolAs the energy consumption of residential building takes a large part in the building energy consumption, it is important to promote energy efficiency in residential building for green development. In order to evaluate the energy consumption of residential building more effectively, this paper proposes a combined prediction model based on random forest and BP neural network (RF-BPNN). To verify the prediction effect of the RF-BPNN combined model, experiments were performed by using the energy efficiency data set in the UCI database, and the model was evaluated with five indicators: mean absolute error, root mean square deviation, mean absolute percentage error, correlation coefficient, and coincidence index. Compared with the random forest, BP neural network model, and other existing models, respectively, it is proven by the experimental results that the RF-BPNN model possesses higher prediction accuracy and better stability.http://dx.doi.org/10.1155/2021/7271383 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xuenan Zhang Jinxin Zhang Jinhua Zhang YuChuan Zhang |
spellingShingle |
Xuenan Zhang Jinxin Zhang Jinhua Zhang YuChuan Zhang Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network Geofluids |
author_facet |
Xuenan Zhang Jinxin Zhang Jinhua Zhang YuChuan Zhang |
author_sort |
Xuenan Zhang |
title |
Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network |
title_short |
Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network |
title_full |
Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network |
title_fullStr |
Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network |
title_full_unstemmed |
Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network |
title_sort |
research on the combined prediction model of residential building energy consumption based on random forest and bp neural network |
publisher |
Hindawi-Wiley |
series |
Geofluids |
issn |
1468-8123 |
publishDate |
2021-01-01 |
description |
As the energy consumption of residential building takes a large part in the building energy consumption, it is important to promote energy efficiency in residential building for green development. In order to evaluate the energy consumption of residential building more effectively, this paper proposes a combined prediction model based on random forest and BP neural network (RF-BPNN). To verify the prediction effect of the RF-BPNN combined model, experiments were performed by using the energy efficiency data set in the UCI database, and the model was evaluated with five indicators: mean absolute error, root mean square deviation, mean absolute percentage error, correlation coefficient, and coincidence index. Compared with the random forest, BP neural network model, and other existing models, respectively, it is proven by the experimental results that the RF-BPNN model possesses higher prediction accuracy and better stability. |
url |
http://dx.doi.org/10.1155/2021/7271383 |
work_keys_str_mv |
AT xuenanzhang researchonthecombinedpredictionmodelofresidentialbuildingenergyconsumptionbasedonrandomforestandbpneuralnetwork AT jinxinzhang researchonthecombinedpredictionmodelofresidentialbuildingenergyconsumptionbasedonrandomforestandbpneuralnetwork AT jinhuazhang researchonthecombinedpredictionmodelofresidentialbuildingenergyconsumptionbasedonrandomforestandbpneuralnetwork AT yuchuanzhang researchonthecombinedpredictionmodelofresidentialbuildingenergyconsumptionbasedonrandomforestandbpneuralnetwork |
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1716844602575552512 |