The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Data science for building energy management: A review
Open accessAbstract
The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.
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Cited by
Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., & Bemporad, A. (2018) · Energies · doi:10.3390/en11030631 · Cited by 637
A deep learning framework for building energy consumption forecast
Somu, N., Raman M R, G., & Ramamritham, K. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110591 · Cited by 500
Mariano-Hernández, D., Hernández-Callejo, L., Zorita-Lamadrid, A., Duque-Pérez, O., & Santos García, F. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101692 · Cited by 484
Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance
Lu, Q., Xie, X., Parlikad, A. K., & Schooling, J. M. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103277 · Cited by 481
Hummel, P., Braun, M., Tretter, M., & Dabrock, P. (2021) · Big Data & Society · doi:10.1177/2053951720982012 · Cited by 422
Metadata
- Authors
- Molina-Solana, M., Ros, M., Ruiz, M. D., Gómez-Romero, J., & Martin-Bautista, M. J.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 70
- Pages
- 598-609
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W2563462907
- Cited by
- 287
- References
- 60
- FWCI
- 29.57
- Type
- journal article
- Language
- en
- Open access
- green
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