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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Main Authors: Xuenan Zhang, Jinxin Zhang, Jinhua Zhang, YuChuan Zhang
Format: Article
Language:English
Published: Hindawi-Wiley 2021-01-01
Series:Geofluids
Online Access:http://dx.doi.org/10.1155/2021/7271383
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spelling 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
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