The Use of The SHAP Algorithm in Building Energy Efficiency: A Bibliometric Analysis
Öz
Anahtar Kelimeler
AI algorithms, building energy efficiency, explainable AI, machine learning algorithms, SHAP algorithm
Kaynakça
- Abdelaziz, A., Santos, V., & Dias, M. S. (2021). Machine learning techniques in the energy consumption of buildings: A systematic literature review using text mining and bibliometric analysis. Energies, 14(22). https://doi.org/10.3390/en14227810
- Alsamraee, S. A., & Khanna, S. (2025). High-resolution energy consumption forecasting of a university campus power plant based on advanced machine learning techniques. Energy Strategy Reviews, 60. https://doi.org/10.1016/j.esr.2025.101769
- Chen, C., Gao, Z., Zhou, X., Wang, M., & Yan, J. (2025). Dynamic energy consumption quota for public buildings based on multi-level classification and data correction. Journal of Building Engineering, 99. https://doi.org/10.1016/j.jobe.2024.111618
- Chen, Z., Xiao, F., Guo, F., & Yan, J. (2023). Interpretable machine learning for building energy management: A state-of-the-art review. Advances in Applied Energy, 9. https://doi.org/10.1016/j.adapen.2023.100123
- Cui, X., Lee, M., Koo, C., & Hong, T. (2024). Energy consumption prediction and household feature analysis for different residential building types using machine learning and SHAP: Toward energy-efficient buildings. Energy and Buildings, 309. https://doi.org/10.1016/j.enbuild.2024.113997
- Geng, S., Wang, Y., Zuo, J., Zhou, Z., Du, H., & Mao, G. (2017). Building life cycle assessment research: A review by bibliometric analysis. Renewable and Sustainable Energy Reviews, 76, 176-184. https://doi.org/10.1016/j.rser.2017.03.068
- Hatami, A. M., Sabour, M. R., Hajbabaie, M. R., & Nematollahi, H. (2022). Global trends of VOSviewer research, emphasizing Environment and Energy areas: A bibliometric analysis during 2000-2020. Environmental Energy and Economic Research, 6(1), 1-11. https://doi.org/10.22097/EEER.2021.301784.1216
- Kangalli Uyar, S. G., Ozbay, B. K., & Dal, B. (2025). Interpretable building energy performance prediction using XGBoost Quantile Regression. Energy and Buildings, 340. https://doi.org/10.1016/j.enbuild.2025.115815
- Kemeç, A., & Altınay, A. T. (2023). Sustainable Energy Research Trend: A Bibliometric Analysis Using VOSviewer, RStudio Bibliometrix, and CiteSpace Software Tools. Sustainability (Switzerland), 15(4). https://doi.org/10.3390/su15043618
- Korsavi, S. S., Azari, R., Iulo, L. D., & Mahdavi, M. (2025). Determinants of U.S. residential energy consumption at national and state levels: Policy implications. Energy Policy, 202. https://doi.org/10.1016/j.enpol.2025.114594