TY - GEN
T1 - Data Driven Thermal Comfort Model for Smart Home Energy Management System
AU - Yelisetti, Srinivas
AU - Saini, Vikash Kumar
AU - Kumar, Rajesh
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Recent years have seen an increase in the popularity of smart and energy-efficient homes. The main issue in developing a control system for such a structure is to reduce energy usage without sacrificing customer satisfaction. This article has proposed a multi-objective paradigm to achieve this goal. Visual, air quality, and thermal comfort are considered. Particle swarm optimization (PSO) is utilised to optimise the overall system. It is still difficult to ensure that all occupants are satisfied with their thermal comfort because of how various people's body temperatures are established. In this paper, a data-driven approach is proposed to predict user thermal comfort in residential houses. The interior comfort temperature of each individual occupant has been predicted using an artificial neural network (ANN) prediction model, which may be utilised as the comfort temperature reference for heating, ventilation, and air conditioning (HVAC) management systems. The proposed model has been compared with single objective comfort maximisation with variable thermal comfort set points for individual occupants and a constant set point for all occupants, and the proposed model has shown its efficacy.
AB - Recent years have seen an increase in the popularity of smart and energy-efficient homes. The main issue in developing a control system for such a structure is to reduce energy usage without sacrificing customer satisfaction. This article has proposed a multi-objective paradigm to achieve this goal. Visual, air quality, and thermal comfort are considered. Particle swarm optimization (PSO) is utilised to optimise the overall system. It is still difficult to ensure that all occupants are satisfied with their thermal comfort because of how various people's body temperatures are established. In this paper, a data-driven approach is proposed to predict user thermal comfort in residential houses. The interior comfort temperature of each individual occupant has been predicted using an artificial neural network (ANN) prediction model, which may be utilised as the comfort temperature reference for heating, ventilation, and air conditioning (HVAC) management systems. The proposed model has been compared with single objective comfort maximisation with variable thermal comfort set points for individual occupants and a constant set point for all occupants, and the proposed model has shown its efficacy.
KW - Heating Ventilation and Air Conditioning Sys-tem
KW - Home Energy Management System
KW - Thermal Comfort
UR - http://www.scopus.com/inward/record.url?scp=85152372303&partnerID=8YFLogxK
U2 - 10.1109/PEDES56012.2022.10080166
DO - 10.1109/PEDES56012.2022.10080166
M3 - Conference contribution
AN - SCOPUS:85152372303
T3 - 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022
BT - 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022
Y2 - 14 December 2022 through 17 December 2022
ER -