The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis
Open accessAbstract
Abstract Building energy use is expected to grow by more than 40% in the next 20 years. Electricity remains the largest energy source consumed by buildings, and that demand is growing. To mitigate the impact of the growing demand, strategies are needed to improve buildings' energy efficiency. In residential buildings home appliances, water, and space heating are answerable for the increase of energy use, while space heating and other miscellaneous equipment are behind the increase of energy utilization in non-residential buildings. Building energy management systems support building managers and proprietors to increase energy efficiency in modern and existing buildings, non-residential and residential buildings can benefit from building energy management system to decrease energy use. Base on the type of building, different management strategies can be used to achieve energy savings. This paper presents a review of management strategies for building energy management systems for improving energy efficiency. Different management strategies are investigated in non-residential and residential buildings. Following this, the reviewed researches are discussed in terms of the type of buildings, building systems, and management strategies. Lastly, the paper discusses future challenges for the increase of energy efficiency in building energy management system.
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Cites
Wang, L., Wang, Z., & Yang, R. (2012) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2011.2178044 · Cited by 235
Cited by
Agostinelli, S., Cumo, F., Guidi, G., & Tomazzoli, C. (2021) · Energies · doi:10.3390/en14082338 · Cited by 280
Aguilar, J., Garces-Jimenez, A., R-Moreno, M. D., & García, R. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111530 · Cited by 278
Kanellopoulos, D., Sharma, V. K., Panagiotakopoulos, T., & Kameas, A. (2023) · Electronics · doi:10.3390/electronics12112490 · Cited by 120
Xie, J., Ajagekar, A., & You, F. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121162 · Cited by 109
Personalized federated learning for buildings energy consumption forecasting
Wang, R., Bai, L., Rayhana, R., & Liu, Z. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114762 · Cited by 38
Metadata
- Authors
- Mariano-Hernández, D., Hernández-Callejo, L., Zorita-Lamadrid, A., Duque-Pérez, O., & Santos García, F.
- Venue
- Journal of Building Engineering
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 33
- Pages
- 101692
- ISSN
- 2352-7102 (ISSN-L)
- OpenAlex
- W3045445587
- Cited by
- 484
- References
- 1
- FWCI
- 29.03
- Type
- journal article
- Language
- en
- Open access
- green
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- Mariano-Hernández, D., Hernández-Callejo, L., Zorita-Lamadrid, A., Duque-Pérez, O., & Santos García, F. (2021). A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis. Journal of Building Engineering. https://doi.org/10.1016/j.jobe.2020.101692
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
