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.
Bibliographical noteKAUST Repository Item: Exported on 2022-04-26
Acknowledgements: 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).
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Mechanics of Materials
- Civil and Structural Engineering
- Building and Construction