A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management
Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building en...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Thermo |
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| Online Access: | https://www.mdpi.com/2673-7264/4/1/8 |
| _version_ | 1797239228035235840 |
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| author | Francesca Villano Gerardo Maria Mauro Alessia Pedace |
| author_facet | Francesca Villano Gerardo Maria Mauro Alessia Pedace |
| author_sort | Francesca Villano |
| collection | DOAJ |
| description | Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems. |
| first_indexed | 2024-04-24T17:48:12Z |
| format | Article |
| id | doaj.art-461257bab2ad4c308338e05aa623964d |
| institution | Directory Open Access Journal |
| issn | 2673-7264 |
| language | English |
| last_indexed | 2024-04-24T17:48:12Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Thermo |
| spelling | doaj.art-461257bab2ad4c308338e05aa623964d2024-03-27T14:06:00ZengMDPI AGThermo2673-72642024-03-014110013910.3390/thermo4010008A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and ManagementFrancesca Villano0Gerardo Maria Mauro1Alessia Pedace2Department of Engineering, Università degli Studi del Sannio, Piazza Roma 21, 82100 Benevento, ItalyDepartment of Engineering, Università degli Studi del Sannio, Piazza Roma 21, 82100 Benevento, ItalySENEA SRL, via John Fitzgerald Kennedy 365, 80125 Napoli, ItalyGiven the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems.https://www.mdpi.com/2673-7264/4/1/8building performance simulationenergy efficiencybuilding optimizationmachine learningdeep learningartificial neural networks |
| spellingShingle | Francesca Villano Gerardo Maria Mauro Alessia Pedace A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management Thermo building performance simulation energy efficiency building optimization machine learning deep learning artificial neural networks |
| title | A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management |
| title_full | A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management |
| title_fullStr | A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management |
| title_full_unstemmed | A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management |
| title_short | A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management |
| title_sort | review on machine deep learning techniques applied to building energy simulation optimization and management |
| topic | building performance simulation energy efficiency building optimization machine learning deep learning artificial neural networks |
| url | https://www.mdpi.com/2673-7264/4/1/8 |
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