Identifying salient features of cooling energy usage of commercial buildings using explainable artificial intelligence

References

• Abediniangerabi, B., Makhmalbaf, A., & Shahandashti, M. (2022). Estimating energy savings of ultra-high-performance fibre-reinforced concrete facade panels at the early design stage of buildings using gradient boosting machines. Advances in Building Energy Research, 16(4), 542–567. https://doi.org/10.1080/17512549.2021.2011410
   Web of Science ®Google Scholar
• Administration, U. S. E. I. (2022). 2018 Commercial buildings energy consumption survey consumption and expenditures highlights. https://www.eia.gov/consumption/commercial/data/2018/pdf/CBECS 2018 CE Release 2 Flipbook.pdf
   Google Scholar
• Administration, U. S. E. I. (n.d.). Commercial buildings energy consumption survey (CBECS). Retrieved January 31, 2023, from https://www.eia.gov/consumption/commercial/
   Google Scholar
• Alam, M. J., & Islam, M. A. (2017). Effect of external shading and window glazing on energy consumption of buildings in Bangladesh. Advances in Building Energy Research, 11(2), 180–192. https://doi.org/10.1080/17512549.2016.1190788
   Web of Science ®Google Scholar
• Amin, M. N., Salami, B. A., Zahid, M., Iqbal, M., Khan, K., Abu-Arab, A. M., Alabdullah, A. A., & Jalal, F. E. (2022). Investigating the bond strength of FRP laminates with concrete using LIGHT GBM and SHAPASH analysis. Polymers, 14(21), 4717. https://doi.org/10.3390/polym14214717
   PubMed Web of Science ®Google Scholar
• Amirifard, F., Sharif, S. A., & Nasiri, F. (2019). Application of passive measures for energy conservation in buildings – A review. Advances in Building Energy Research, 13(2), 282–315. https://doi.org/10.1080/17512549.2018.1488617
   Web of Science ®Google Scholar
• Andrić, I., Le Corre, O., Lacarrière, B., Ferrão, P., & Al-Ghamdi, S. G. (2021). Initial approximation of the implications for architecture due to climate change. Advances in Building Energy Research, 15(3), 337–367. https://doi.org/10.1080/17512549.2018.1562980
   Web of Science ®Google Scholar
• Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. (2021). Explainable artificial intelligence: An analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(5), 1–13. https://doi.org/10.1002/widm.1424
   Web of Science ®Google Scholar
• Aste, N., Manfren, M., & Marenzi, G. (2017). Building automation and control systems and performance optimization: A framework for analysis. Renewable and Sustainable Energy Reviews, 75(October 2016), 313–330. https://doi.org/10.1016/j.rser.2016.10.072
   Google Scholar
• Bekkouche, S. M. A., Benouaz, T., Hamdani, M., Cherier, M. K., Yaiche, M. R., & Benamrane, N. (2017). Diagnosis and comprehensive quantification of energy needs for existing residential buildings under Sahara weather conditions. Advances in Building Energy Research, 11(1), 37–51. https://doi.org/10.1080/17512549.2015.1119059
   Web of Science ®Google Scholar
• Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7
   Web of Science ®Google Scholar
• Campagna, L. M., & Fiorito, F. (2022). On the impact of climate change on building energy consumptions: A meta-analysis. Energies, 15(1). https://doi.org/10.3390/en15010354
   Web of Science ®Google Scholar
• Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. https://doi.org/10.1016/j.enbuild.2016.06.089
   Web of Science ®Google Scholar
• Choi, D., & Kim, C. (2021). Diagnosis of building energy consumption in the 2012 CBECS data using heterogeneous effect of energy variables : A recursive partitioning approach. 1737–1755.
   Google Scholar
• Chou, J. S., & Bui, D. K. (2014). Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Energy and Buildings, 82(2014), 437–446. https://doi.org/10.1016/j.enbuild.2014.07.036
   Google Scholar
• Chung, W. (2011). Review of building energy-use performance benchmarking methodologies. Applied Energy, 88(5), 1470–1479. https://doi.org/10.1016/j.apenergy.2010.11.022
   Web of Science ®Google Scholar
• Cyamopsis, B., & Taub, L. (2020). Assessment of the different machine learning models for prediction of cluster assessment of the different machine learning models for prediction of cluster bean (Cyamopsis tetragonoloba L . Taub .) Yield. December. https://doi.org/10.9734/air/2020/v21i930238
   Google Scholar
• Delzendeh, E., Wu, S., Lee, A., & Zhou, Y. (2017). The impact of occupants’ behaviours on building energy analysis: A research review. Renewable and Sustainable Energy Reviews, 80(September 2016), 1061–1071. https://doi.org/10.1016/j.rser.2017.05.264
   Google Scholar
• Deng, H, Fannon, D, & Eckelman, M J. (2018). Predictive modeling for US commercial building energy use: A comparison of exiting statistical and machine learning algorithms using CBECS microdata. Energy and Building, 163, 34–43.
   Web of Science ®Google Scholar
• EIA. (2021). Today in energy. https://www.eia.gov/todayinenergy/detail.php?id=47736
   Google Scholar
• Freeman, S. L., Niefer, M. J., & Roop, J. M. (1997). Measuring industrial energy intensity: Practical issues and problems. Energy Policy, 25(7–9), 703–714. https://doi.org/10.1016/S0301-4215(97)00062-1
   Web of Science ®Google Scholar
• Ghosh, I., Chaudhuri, T. D., Alfaro-Cortés, E., Gámez, M., & García, N. (2022). A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence. Technological Forecasting and Social Change, 181(May). https://doi.org/10.1016/j.techfore.2022.121757
   PubMedGoogle Scholar
• Ghosh, I., & Sanyal, M. K. (2021). Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI. International Journal of Information Management Data Insights, 1(2), 100039. https://doi.org/10.1016/j.jjimei.2021.100039
   Google Scholar
• Henn, S., Richarz, J., Maier, L., Ying, X., Osterhage, T., Mehrfeld, P., & Müller, D. (2022). Influences of usage intensity and weather on optimal building energy system design with multiple storage options. Energy and Buildings, 270, 112222. https://doi.org/10.1016/j.enbuild.2022.112222
   Web of Science ®Google Scholar
• Holodinsky, J. K., Yu, A. Y. X., Kapral, M. K., & Austin, P. C. (2021). Using random forests to model 90-day hometime in people with stroke. BMC Medical Research Methodology, 21(1), 1–12. https://doi.org/10.1186/s12874-020-01190-w
   PubMed Web of Science ®Google Scholar
• Horning, N. (2010). Random forests: An algorithm for image classification and generation of continuous fields data sets. International conference on geoinformatics for spatial infrastructure development in earth and allied sciences 2010, 1–6.
   Google Scholar
• Hu, S., Yan, D., & Qian, M. (2019). Using bottom-up model to analyze cooling energy consumption in China’s urban residential building. Energy and Buildings, 202. https://doi.org/10.1016/j.enbuild.2019.109352
   Web of Science ®Google Scholar
• Khatibi, A., Jahangir, M. H., & Astaraei, F. R. (2023). Developing an IoT-based electrochromic windows for smart buildings. Advances in Building Energy Research. https://doi.org/10.1080/17512549.2023.2175371
   Google Scholar
• Kim, M., Jun, J. A., Song, Y. J., & Pyo, C. S. (2020). Explanation for building energy prediction. International conference on ICT convergence, 2020-Octob, 1168–1170. https://doi.org/10.1109/ICTC49870.2020.9289340
   Google Scholar
• Levinson, A. (2016). How much energy do building energy codes save? Evidence from California houses. American Economic Review, 106(10), 2867–2894. https://doi.org/10.1257/aer.20150102
   Web of Science ®Google Scholar
• Li, M., Nanda, G., Chhajedss, S. S., & Sundararajan, R. (2020). Machine learning-based decision support system for early detection of breast cancer. Indian Journal of Pharmaceutical Education and Research, 54(3), S705–S715. https://doi.org/10.5530/ijper.54.3s.171
   Web of Science ®Google Scholar
• Li, Y., Zou, C., Berecibar, M., Nanini-Maury, E., Chan, J. C. W., van den Bossche, P., Van Mierlo, J., & Omar, N. (2018). Random forest regression for online capacity estimation of lithium-ion batteries. Applied Energy, 232(February), 197–210. https://doi.org/10.1016/j.apenergy.2018.09.182
   Google Scholar
• Lokhandwala, M., & Nateghi, R. (2018). Leveraging advanced predictive analytics to assess commercial cooling load in the U.S. Sustainable Production and Consumption, 14, 66–81. https://doi.org/10.1016/j.spc.2018.01.001
   Web of Science ®Google Scholar
• Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2017-Decem(Section 2), 4766–4775.
   Google Scholar
• Macedo, L., Miguel Matos, L., Cortez, P., Domingues, A., Moreira, G., & Pilastri, A. (2022). A machine learning approach for spare parts lifetime estimation. April, 765–772. https://doi.org/10.5220/0010903800003116
   Google Scholar
• Mehta, M. (n.d.). Explainable AI : Foundations, methodologies and applications. https://link.springer.com/book/10.1007/978-3-031-12807-3
   Google Scholar
• Molnar, C. (2023). Interpreting machine learning models with SHAP: A guide with Python examples and theory on Shapley Values.
   Google Scholar
• Pradhan, P., Behera, P. K., & Ray, B. N. B. (2016). Modified round robin algorithm for resource allocation in cloud computing. Procedia Computer Science, 85(Cms), 878–890. https://doi.org/10.1016/j.procs.2016.05.278
   Google Scholar
• Qi, Y. (2012). Ensemble machine learning. Ensemble Machine Learning. https://doi.org/10.1007/978-1-4419-9326-7
   Google Scholar
• Qiu, Y. (2014). Energy efficiency and rebound effects : An econometric. 295–335. https://doi.org/10.1007/s10640-013-9729-9
   Google Scholar
• Riahi, G. (2015). E-learning systems based on cloud computing: A review. Procedia Computer Science, 62(Scse), 352–359. https://doi.org/10.1016/j.procs.2015.08.415
   Google Scholar
• Saboni, A., Ouamane, M. R., Bennis, O., & Kratz, F. (2022). Model reports, a supervision tool for machine learning engineers and users. International Journal of Education and Information Technologies, 16(February), 50–54. https://doi.org/10.46300/9109.2022.16.5
   Google Scholar
• Santamouris, M. (2016). Cooling the buildings – Past, present and future. Energy and Buildings, 128, 617–638. https://doi.org/10.1016/j.enbuild.2016.07.034
   Web of Science ®Google Scholar
• Sathe, S. S., & Mahalle, P. (2023). Predictive analytics in financial services using explainable AI. International Conference on Next Generation Systems and Networks, 431–444. https://doi.org/10.1007/978-981-99-0483-9_35
   Google Scholar
• Senarathne, L. R., Nanda, G., & Sundararajan, R. (2022). Influence of building parameters on energy efficiency levels: A Bayesian network study. Advances in Building Energy Research, 16(6), 780–805. https://doi.org/10.1080/17512549.2022.2108142
   Web of Science ®Google Scholar
• Seyedzadeh, S., Rahimian, F. P., Glesk, I., & Roper, M. (2018). Machine learning for estimation of building energy consumption and performance: A review. Visualization in Engineering, 6(1). https://doi.org/10.1186/s40327-018-0064-7
   Google Scholar
• Shapash. (n.d.). Retrieved November 30, 2022, from https://shapash.readthedocs.io/en/latest/index.html%0A
   Google Scholar
• Singh, A., Thakur, N., & Sharma, A. (2016). A review of supervised machine learning algorithms. Proceedings of the 10th INDIACom; 2016 3rd international conference on computing for sustainable global development, INDIACom 2016, 1310–1315.
   Google Scholar
• Štrumbelj, E., & Kononenko, I. (2014). Explaining prediction models and individual predictions with feature contributions. Knowledge and Information Systems, 41(3), 647–665. https://doi.org/10.1007/s10115-013-0679-x
   Web of Science ®Google Scholar
• Tian, Z., Si, B., Shi, X., & Fang, Z. (2019). An application of Bayesian network approach for selecting energy efficient HVAC systems. Journal of Building Engineering, 25(November 2018), 100796. https://doi.org/10.1016/j.jobe.2019.100796
   Google Scholar
• Wang, Z., Liu, J., Zhang, Y., Yuan, H., Zhang, R., & Srinivasan, R. S. (2021). Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles. Renewable and Sustainable Energy Reviews, 143(August 2020), 110929. https://doi.org/10.1016/j.rser.2021.110929
   Google Scholar
• Xie, X., Wu, T., Zhu, M., Jiang, G., Xu, Y., Wang, X., & Pu, L. (2021). Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land. Ecological Indicators, 120, 106925. https://doi.org/10.1016/j.ecolind.2020.106925
   Web of Science ®Google Scholar
• Yan, L., & Liu, M. (2020). A simplified prediction model for energy use of air conditioner in residential buildings based on monitoring data from the cloud platform. Sustainable Cities and Society, 60(June 2019), 102194. https://doi.org/10.1016/j.scs.2020.102194
   Google Scholar
• Zhao, H. X., & Magoulès, F. (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6), 3586–3592. https://doi.org/10.1016/j.rser.2012.02.049
   Web of Science ®Google Scholar