A weather-clustering and energy-thermal comfort optimization methodology for indoor cooling in subtropical desert climates
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Embargo End Date:
2024-03-16
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Science and Engineering (CEMSE) DivisionCenter of Excellence for NEOM Research
Office of the President
Applied Mathematics and Computational Science Program
Date
2022-03-16Embargo End Date
2024-03-16Permanent link to this record
http://hdl.handle.net/10754/676455
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This paper proposes a novel methodology to assess the yearly Heating Ventilation Air Conditioning (HVAC) energy costs and indoor comfort levels for indoor spaces. The methodology involves a weather clustering technique coupled with a simulation-based multi-objective optimization for HVAC systems operation control. The clustering technique is utilized to determine representative days that capture the yearly variability of outdoor air temperature, total solar radiation on horizontal surface, wind speed, and outdoor relative humidity from historical time series. The optimization framework then determines the optimal cooling operation strategies that simultaneously minimize energy consumption cost and thermal discomfort for each representative day. Such clustering-based approach, particularly, enables the assessment of the annual operation of the HVAC using representative daily weather conditions while avoiding the high computational costs of a day-by-day optimization. The numerical prospects of the proposed framework are illustrated using an office building located in Saudi Arabia, i.e., under subtropical desert conditions. The results show that the proposed methodology can achieve reductions of up to 17.6% and 19.4% in annual cooling consumption cost and thermal discomfort, respectively, compared to standard baseline policies.Citation
Souayfane, F., Lima, R. M., Dahrouj, H., & Knio, O. (2022). A weather-clustering and energy-thermal comfort optimization methodology for indoor cooling in subtropical desert climates. Journal of Building Engineering, 104327. https://doi.org/10.1016/j.jobe.2022.104327Sponsors
The authors wish to thank Ibrahim Hoteit for the helpful discussions and for providing the weather data files utilized in the paper numerical results. This work was supported in part by the Center of Excellence for NEOM Research at the King Abdullah University of Science and Technology (KAUST).Publisher
Elsevier BVJournal
Journal of Building EngineeringAdditional Links
https://linkinghub.elsevier.com/retrieve/pii/S2352710222003400ae974a485f413a2113503eed53cd6c53
10.1016/j.jobe.2022.104327