ABSTRACT
We develop a probabilistic method for predicting the thermal comfort of residential occupants during demand response (DR) events. Specifically, we estimate the probability that occupants will change the thermostat setpoint, by calculating their discomfort tolerance based on the degree and duration of discomfort. We also show that we can predict this discomfort tolerance reliably.
The primary advantage of our approach is that it requires minimal data, in contrast with other thermal comfort prediction models, i.e., only historical thermostat setpoints from connected thermostats (CTs) and weather data. Since CTs are a prerequisite for DR event participation, this approach requires no additional capital on the part of the utility and is nonintrusive for the customer. At the same time, accurate predictions of occupant comfort allow utilities to tailor DR events for each customer to minimize the likelihood of customer opt-out while maximizing load flexibility.
- P.O. Fanger, 1988. Fundamentals of thermal comfort. Advances in Solar Energy Technology. Pergamon, 3056-3061.Google Scholar
- R.J. De Dear , 1988. Developing an adaptive model of thermal comfort and preference/discussion. ASHRAE transactions 104 (1998): 145.Google Scholar
- J.F. Nicol and M.A. Humphreys, 2002. Adaptive thermal comfort and sustainable thermal standards for buildings. Energy and buildings 34.6 (2002): 563-572.Google Scholar
- ASHRAE Standard 55: Thermal Environmental Conditions for Human Occupancy. 2020.Google Scholar
- EN 16798-1: Energy performance of buildings - Ventilation for buildings - Part 1. EN 16798-1. 2019.Google Scholar
- D. Enescu, 2017. A review of thermal comfort models and indicators for indoor environments. Renew. and Sust. Energy Reviews 79 (2017): 1353-1379.Google ScholarCross Ref
- S. Carlucci , 2018. Review of adaptive thermal comfort models in built environmental regulatory documents. Building and Environment 137 (2018): 73-89.Google ScholarCross Ref
- F. Auffenberg , 2017. A comfort-based approach to smart heating and air conditioning. ACM Trans. on Intelligent Sys. and Tech. (TIST) 9.3 (2017):1-20.Google Scholar
- E. Henderson , 2018. Programmable communicating thermostats and load controllers as heating season demand-response strategy for electricity load shifting in residential buildings. ACEEE Summ. Study on Energy Eff. in Bldgs.Google Scholar
- J. Ryu , 2020. Quantifying householder tolerance of thermal discomfort before turning on air-conditioner. Energy and Buildings 211 (2020): 109797.Google ScholarCross Ref
Index Terms
- Thermal discomfort prediction with sparse residential thermostat dataset
Recommendations
Thermal Simulation of a Supermarket Cold Zone with Integrated Assessment of Human Thermal Comfort
Computational Science and Its Applications – ICCSA 2020AbstractThis work seeks to analyze the thermal comfort of the occupants in a large building of Commerce and Services, integrating measures of assessment and energy efficiency promotion. The building is still in the construction phase and at its conclusion,...
A Human Thermal Comfort Level Estimating Method Using Thermal Image and Sensor Data
iiWAS2021: The 23rd International Conference on Information Integration and Web IntelligenceRecently, it has become a trend to construct a thermal environment with human thermal comfort. The advantage of human thermal comfort is that we could adjust the environment through the thermal sensation of human. Since human thermal comfort is ...
Thermal Comfort and Indoor Air Quality of Task Ambient Air Conditioning in Modern Office Buildings
ICIII '09: Proceedings of the 2009 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 02Energy and environmental sustainability is a major global trend for the 21st century. Thermal discomfort and poor air quality in office buildings can result in loss of productivity, absenteeism and medical problems. The most frequently identified causes ...
Comments