Browsing by Author "Genc, Sahika"

Now showing 1 - 2 of 2

Citation - WoS: 16
Citation - Scopus: 20
Cloud-based model predictive building thermostatic controls of commercial buildings: Algorithm and implementation
(Institute of Electrical and Electronics Engineers Inc., 2015) Emrah Biyik; James D. Brooks; Hullas Sehgal; Jigar J. Shah; Sahika Gency; Shah, Jigar; Gency, Sahika; Biyik, Emrah; Brooks, James D.; Sehgal, Hullas; Genc, Sahika
The contribution of this paper is in two-folds: 1) If more predictive and intelligent control of the thermostat setpoints with no explicit models of Root Top Units (RTUs) yet with simplistic lumped parameter thermal models of buildings can be effective in reducing a small commercial buildings summer-time peak load while adequately maintaining comfort levels and 2) how this simplistic indirect control approach to RTUs compare to more sophisticated direct control approaches in terms of peak-load reduction and cost. First the modelpredictive control approach is presented. Second the results of cloud-based implementation of the optimization algorithm at the two demonstration commercial buildings owned by General Electric (GE) optimizer characteristics different set point trajectories and their implication with regards to peak load and comfort and observations are described. On average the savings from the indirect optimal control strategy utilized in our approach through a cloud-based control implementation architecture is shown to be comparable to previously stated savings in literature from more sophisticated direct optimal control of RTUs while the comfort levels are the same as the non-optimal strategy or slightly better in some cases. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 8
Citation - Scopus: 10
Model Predictive Building Thermostatic Controls of Small-to-Medium Commercial Buildings for Optimal Peak Load Reduction Incorporating Dynamic Human Comfort Models: Algorithm and Implementation
(IEEE, 2014) Emrah Biyik; Sahika Genc; James D. Brooks; Biyik, Emrah; Brooks, James D.; Genc, Sahika
The peak kW of a typical New York State office building is thought to primarily be a function of the HVAC system often the buildings largest load but may also be influenced by occupancy and other loads. First a simple lumped parameter model with a minimum amount of building's physical input data and trained with actual thermal and electrical data is considered to approximate the thermal/electric consumption performance of the building and HVAC system on a zonal basis. Then the lumped parameter model integrated with a dynamic human comfort model is used to develop optimized zonal thermostat setpoint schedules to minimize the cooling systems contribution to the buildings peak power load while maintaining human comfort at a desired level. A 24-hour weather and occupancy forecasts are also incorporated into the optimization algorithm. The key difference of our approach compared to previous approaches that utilize model-predictive control is that a minimal set of measurement profiles are utilized to reduce the installation cost resulting in a cost effective advanced controls solution for a large number of small and medium size office buildings. The model predictive optimization approach is implemented at multiple demonstration sites. The hardware architecture and software platform installed at one of the demonstration buildings are discussed. Finally it is demonstrated that the proposed controller can effectively minimize peak cooling load on the HVAC equipment while achieving a satisfactory thermal comfort inside the building.