Influence of a Better Prediction of Thermal Satisfaction for the Implementation of an HVAC-Based Demand Response Strategy

• Published in: Energies (Basel) Vol. 15; no. 9; p. 3094
• Main Authors: Lim, Jongyeon, Choi, Wonjun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 23.04.2022
• Subjects: Air conditioning; Algorithms; demand response strategy; Energy; Energy consumption; Energy management; Environmental conditions; Facilities management; HVAC; occupant performance; Operators; predicted mean vote; Prediction models; Productivity; Regression analysis; Software; thermal comfort; Thermal environments; thermal satisfaction; thermal sensation
• DOI: 10.3390/en15093094
• ISSN: 1996-1073

Building system operation faces the challenge of reducing energy use and implementing a demand response, which can be defined as a temporary modification in energy loads affecting dynamic energy price and reliability information. The heating, ventilation, and air-conditioning (HVAC) system in buildings provides an opportunity for implementing demand response strategies due to the thermal inertia in building zones. However, an HVAC-based demand response is not a prevalent strategy in actual facility management due to the lack of understanding among building operators of their facilities and occupants. Herein, we focus on developing a better understanding of the occupant side by obtaining a reliable prediction of occupants’ thermal satisfaction. We evaluate the prediction performance of a probabilistic model provided in our previous paper using a case study with a subset of the ASHRAE Global Thermal Comfort Database II. The influence of a better prediction of thermal satisfaction on the implementation of the HVAC-based demand response strategy is further discussed. The conventional method overestimates productivity deterioration due to changes in the thermal environment, making it challenging to implement an HVAC-based demand response strategy aggressively. A robust prediction model using a probabilistic approach can solve this problem, allowing building operators to adopt an aggressive stance for implementing a demand response. The results of this study offer fresh insight into the impact of a probabilistic model in the prediction of thermal satisfaction for establishing an HVAC-based demand response strategy.