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

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 buildi...

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Bibliographic Details
Published inEnergies (Basel) Vol. 15; no. 9; p. 3094
Main Authors Lim, Jongyeon, Choi, Wonjun
Format Journal Article
LanguageEnglish
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
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15093094