Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques
With the global increase in demand for energy, energy conservation of research and development buildings has become of primary importance for building owners. Knowledge based on the patterns in energy consumption of previous years could be used to predict the near-future energy usage of buildings, t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/7/1847 |
| _version_ | 1797570989602635776 |
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| author | Jun-Mao Liao Ming-Jui Chang Luh-Maan Chang |
| author_facet | Jun-Mao Liao Ming-Jui Chang Luh-Maan Chang |
| author_sort | Jun-Mao Liao |
| collection | DOAJ |
| description | With the global increase in demand for energy, energy conservation of research and development buildings has become of primary importance for building owners. Knowledge based on the patterns in energy consumption of previous years could be used to predict the near-future energy usage of buildings, to optimize and facilitate more effective energy consumption. Hence, this research aimed to develop a generic model for predicting energy consumption. Air-conditioning was used to exemplify the generic model for electricity consumption, as it is the process that often consumes the most energy in a public building. The purpose of this paper is to present this model and the related findings. After causative factors were determined, the methods of linear regression and various machine learning techniques—including the earlier machine learning techniques of support vector machine, random forest, and multilayer perceptron, and the later machine learning techniques of deep neural network, recurrent neural network, long short-term memory, and gated recurrent unit—were applied for prediction. Among them, the prediction of random forest resulted in an R<sup>2</sup> of 88% ahead of the first month and 81% ahead of the third month. These experimental results demonstrate that the prediction model is reliable and significantly accurate. Building owners could further enrich the model for energy conservation and management. |
| first_indexed | 2024-03-10T20:33:12Z |
| format | Article |
| id | doaj.art-ae25002c9abc451894b8f17fca2d4c6c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T20:33:12Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ae25002c9abc451894b8f17fca2d4c6c2023-11-19T21:14:42ZengMDPI AGEnergies1996-10732020-04-01137184710.3390/en13071847Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning TechniquesJun-Mao Liao0Ming-Jui Chang1Luh-Maan Chang2Department of Civil Engineering, National Taiwan University, No. 1, Sec. 4 Roosevelt Road, Taipei 10617, TaiwanResearch Center of Climate Change and Sustainable Development, National Taiwan University, No. 1, Sec. 4 Roosevelt Road, Taipei 10617, TaiwanDepartment of Civil Engineering, National Taiwan University, No. 1, Sec. 4 Roosevelt Road, Taipei 10617, TaiwanWith the global increase in demand for energy, energy conservation of research and development buildings has become of primary importance for building owners. Knowledge based on the patterns in energy consumption of previous years could be used to predict the near-future energy usage of buildings, to optimize and facilitate more effective energy consumption. Hence, this research aimed to develop a generic model for predicting energy consumption. Air-conditioning was used to exemplify the generic model for electricity consumption, as it is the process that often consumes the most energy in a public building. The purpose of this paper is to present this model and the related findings. After causative factors were determined, the methods of linear regression and various machine learning techniques—including the earlier machine learning techniques of support vector machine, random forest, and multilayer perceptron, and the later machine learning techniques of deep neural network, recurrent neural network, long short-term memory, and gated recurrent unit—were applied for prediction. Among them, the prediction of random forest resulted in an R<sup>2</sup> of 88% ahead of the first month and 81% ahead of the third month. These experimental results demonstrate that the prediction model is reliable and significantly accurate. Building owners could further enrich the model for energy conservation and management.https://www.mdpi.com/1996-1073/13/7/1847building energy conservationresearch and development buildingelectricity consumptionmachine learningdeep learning |
| spellingShingle | Jun-Mao Liao Ming-Jui Chang Luh-Maan Chang Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques Energies building energy conservation research and development building electricity consumption machine learning deep learning |
| title | Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques |
| title_full | Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques |
| title_fullStr | Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques |
| title_full_unstemmed | Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques |
| title_short | Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques |
| title_sort | prediction of air conditioning energy consumption in r d building using multiple machine learning techniques |
| topic | building energy conservation research and development building electricity consumption machine learning deep learning |
| url | https://www.mdpi.com/1996-1073/13/7/1847 |
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