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: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2021/7271383 |
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| _version_ | 1832561582049067008 |
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| author | Xuenan Zhang Jinxin Zhang Jinhua Zhang YuChuan Zhang |
| author_facet | Xuenan Zhang Jinxin Zhang Jinhua Zhang YuChuan Zhang |
| author_sort | Xuenan Zhang |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-9ff8a1eebbc54d51b2e54d09e2585c40 |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-9ff8a1eebbc54d51b2e54d09e2585c402025-02-03T01:24:42ZengWileyGeofluids1468-81151468-81232021-01-01202110.1155/2021/72713837271383Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural NetworkXuenan Zhang0Jinxin Zhang1Jinhua Zhang2YuChuan Zhang3Business School, Hubei University, Wuhan 430062, ChinaBusiness School, Hubei University, Wuhan 430062, ChinaSchool of Economics and Management, Fuzhou University, Fuzhou 350108, ChinaBusiness School, Hubei University, Wuhan 430062, ChinaAs 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 |
| 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 |
| title | 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_short | 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 |
| 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 |