The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
Machine learning for estimation of building energy consumption and performance: a review
Open accessAbstract
Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.
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Citations
Cites
EnergyPlus: creating a new-generation building energy simulation program
Crawley, D. B., Lawrie, L. K., Winkelmann, F. C., Buhl, W. F., Huang, Y. J., Pedersen, C. O., Strand, R. K., Liesen, R. J., Fisher, D. E., Witte, M. J., et al. (2001) · Energy and Buildings · doi:10.1016/s0378-7788(00)00114-6 · Cited by 2,822
A review on the prediction of building energy consumption
Zhao, H.-X., & Magoulès, F. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.02.049 · Cited by 1,937
Unsupervised optimal fuzzy clustering
Gath, I., & Geva, A. B. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.192473 · Cited by 1,640
Existing building retrofits: Methodology and state-of-the-art
Ma, Z., Cooper, P., Daly, D., & Ledo, L. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.08.018 · Cited by 1,234
Zero Energy Building – A review of definitions and calculation methodologies
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A review on applications of ANN and SVM for building electrical energy consumption forecasting
Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H., & Saidur, R. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.01.069 · Cited by 954
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Applications of artificial neural-networks for energy systems
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Cited by
Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104440 · Cited by 1,007
“Seyedzadeh et al. [166] have also provided a review on the use of four main ML approaches including ANN, support vector machine, Gaussian-based regressions and clustering, in forecasting and improving building energy performance.”
MethodMachine Learning for Smart and Energy-Efficient Buildings
Das, H. P., Lin, Y.-W., Agwan, U., Spangher, L., Devonport, A., Yang, Y., Drgoňa, J., Chong, A., Schiavon, S., & Spanos, C. J. (2024) · Environmental Data Science · doi:10.1017/eds.2023.43 · Cited by 78
Bui, V., Le, N. T., Nguyen, V. H., Kim, J., & Jang, Y. M. (2021) · Electronics · doi:10.3390/electronics10091026 · Cited by 25
Shariff, S. M. (2022) · European Journal of Electrical Engineering and Computer Science · doi:10.24018/ejece.2022.6.3.435 · Cited by 17
Metadata
- Authors
- Seyedzadeh, S., Rahimian, F. P., Glesk, I., & Roper, M.
- Venue
- Visualization in Engineering
- Published
- 2018-10-02
- Volume
- 6
- Issue
- 1
- ISSN
- 2213-7459 (ISSN-L)
- OpenAlex
- W2894665398
- Cited by
- 482
- References
- 101
- FWCI
- 26.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
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