Research on Energy Consumption Prediction Models for High-Rise Hotels in Guangzhou, Based on Different Machine Learning Algorithms
With the advancement of information technology, energy consumption prediction models are widely used for various types of buildings (office, residential, and commercial buildings) as guidance during the design and management stages. This article will establish an efficient building energy consumptio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/14/2/356 |
| _version_ | 1797298683538046976 |
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| author | Jin Zhang Chuyan Yuan Junyi Yang Lihua Zhao |
| author_facet | Jin Zhang Chuyan Yuan Junyi Yang Lihua Zhao |
| author_sort | Jin Zhang |
| collection | DOAJ |
| description | With the advancement of information technology, energy consumption prediction models are widely used for various types of buildings (office, residential, and commercial buildings) as guidance during the design and management stages. This article will establish an efficient building energy consumption prediction model for hotel buildings. To achieve this, we collected 78 architectural drawings of high-rise hotel buildings to establish 6 kinds of typical energy consumption models in 2 standard floor layouts and 3 public area levels. Then, on this basis, we used the total energy consumption calculated by EnergyPlus as an indicator to conduct sensitivity analysis on geometric feature parameters, internal heat source parameters, and thermal parameters, respectively. Finally, we generated a building database with 5000 samples through the R programming language to calculate and verify the energy consumption. As a result, it was proved that the energy consumption of hotel buildings can be predicted accurately, and that quadratic polynomial regression, with the best accuracy and stability, is the most suitable optimization model for hotel energy consumption prediction in Guangzhou. These conclusions provide a good theoretical basis for the analysis, prediction, and optimization of energy consumption in high-rise hotel buildings in the future. |
| first_indexed | 2024-03-07T22:39:34Z |
| format | Article |
| id | doaj.art-47f0eb57ecde472ca10fd965a4703637 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-07T22:39:34Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-47f0eb57ecde472ca10fd965a47036372024-02-23T15:10:00ZengMDPI AGBuildings2075-53092024-01-0114235610.3390/buildings14020356Research on Energy Consumption Prediction Models for High-Rise Hotels in Guangzhou, Based on Different Machine Learning AlgorithmsJin Zhang0Chuyan Yuan1Junyi Yang2Lihua Zhao3Pearl River Foreign Investment Architectural Designing Institute Co., Ltd., Guangzhou 510060, ChinaCollege of Architecture, Changsha University of Science and Technology, Changsha 410114, ChinaState Key Laboratory of Subtropical Building Science, School of Architecture, South China University of Technology, Guangzhou 510655, ChinaState Key Laboratory of Subtropical Building Science, School of Architecture, South China University of Technology, Guangzhou 510655, ChinaWith the advancement of information technology, energy consumption prediction models are widely used for various types of buildings (office, residential, and commercial buildings) as guidance during the design and management stages. This article will establish an efficient building energy consumption prediction model for hotel buildings. To achieve this, we collected 78 architectural drawings of high-rise hotel buildings to establish 6 kinds of typical energy consumption models in 2 standard floor layouts and 3 public area levels. Then, on this basis, we used the total energy consumption calculated by EnergyPlus as an indicator to conduct sensitivity analysis on geometric feature parameters, internal heat source parameters, and thermal parameters, respectively. Finally, we generated a building database with 5000 samples through the R programming language to calculate and verify the energy consumption. As a result, it was proved that the energy consumption of hotel buildings can be predicted accurately, and that quadratic polynomial regression, with the best accuracy and stability, is the most suitable optimization model for hotel energy consumption prediction in Guangzhou. These conclusions provide a good theoretical basis for the analysis, prediction, and optimization of energy consumption in high-rise hotel buildings in the future.https://www.mdpi.com/2075-5309/14/2/356building energy consumption predictionhigh-rise hotel buildingstypical building modelsmachine learning |
| spellingShingle | Jin Zhang Chuyan Yuan Junyi Yang Lihua Zhao Research on Energy Consumption Prediction Models for High-Rise Hotels in Guangzhou, Based on Different Machine Learning Algorithms Buildings building energy consumption prediction high-rise hotel buildings typical building models machine learning |
| title | Research on Energy Consumption Prediction Models for High-Rise Hotels in Guangzhou, Based on Different Machine Learning Algorithms |
| title_full | Research on Energy Consumption Prediction Models for High-Rise Hotels in Guangzhou, Based on Different Machine Learning Algorithms |
| title_fullStr | Research on Energy Consumption Prediction Models for High-Rise Hotels in Guangzhou, Based on Different Machine Learning Algorithms |
| title_full_unstemmed | Research on Energy Consumption Prediction Models for High-Rise Hotels in Guangzhou, Based on Different Machine Learning Algorithms |
| title_short | Research on Energy Consumption Prediction Models for High-Rise Hotels in Guangzhou, Based on Different Machine Learning Algorithms |
| title_sort | research on energy consumption prediction models for high rise hotels in guangzhou based on different machine learning algorithms |
| topic | building energy consumption prediction high-rise hotel buildings typical building models machine learning |
| url | https://www.mdpi.com/2075-5309/14/2/356 |
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