The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach
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Citations
Cites
Thermal comfort: Analysis and applications in environmental engineering
(1972) · Applied Ergonomics · doi:10.1016/s0003-6870(72)80074-7 · Cited by 4,451
A review on simulation-based optimization methods applied to building performance analysis
Nguyen, A.-T., Reiter, S., & Rigo, P. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.08.061 · Cited by 1,379
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
Applications of artificial neural-networks for energy systems
Kalogirou, S. A. (2000) · Applied Energy · doi:10.1016/s0306-2619(00)00005-2 · Cited by 933
Applying support vector machines to predict building energy consumption in tropical region
Dong, B., Cao, C., & Lee, S. E. (2005) · Energy and Buildings · doi:10.1016/j.enbuild.2004.09.009 · Cited by 800
Magnier, L., & Haghighat, F. (2010) · Building and Environment · doi:10.1016/j.buildenv.2009.08.016 · Cited by 725
Statistical tools for simulation practitioners
(1990) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(90)90147-b · Cited by 528
Asadi, E., Silva, M. G. D., Antunes, C. H., Dias, L., & Glicksman, L. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.06.009 · Cited by 523
Artificial neural networks for the prediction of the energy consumption of a passive solar building
Kalogirou, S. A. (2000) · Energy · doi:10.1016/s0360-5442(99)00086-9 · Cited by 470
Cited by
A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting
Sajjad, M., Khan, Z. A., Ullah, A., Hussain, T., Ullah, W., Lee, M. Y., & Baik, S. W. (2020) · IEEE Access · doi:10.1109/access.2020.3009537 · Cited by 528
Machine learning for estimation of building energy consumption and performance: a review
Seyedzadeh, S., Rahimian, F. P., Glesk, I., & Roper, M. (2018) · Visualization in Engineering · doi:10.1186/s40327-018-0064-7 · Cited by 482
Modeling techniques used in building HVAC control systems: A review
Afroz, Z., Shafiullah, G., Urmee, T., & Higgins, G. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.10.044 · Cited by 411
A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
Li, Y., Chen, H., Yu, P., & Yang, L. (2025) · Applied Sciences · doi:10.3390/app15031476 · Cited by 76
Khayatian, F., Sarto, L., & Dall'O', G. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.08.237 · Cited by 40
Metadata
- Authors
- Ascione, F., Bianco, N., De Stasio, C., Mauro, G. M., & Vanoli, G. P.
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 118
- Pages
- 999-1017
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W2548560601
- Cited by
- 258
- References
- 51
- FWCI
- 24.52
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Ascione, F., Bianco, N., De Stasio, C., Mauro, G. M., & Vanoli, G. P. (2017). Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach. Energy. https://doi.org/10.1016/j.energy.2016.10.126
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.
