Abstract
The residential building sector is encouraged to participate in demand response (DR) programs owing to its flexible and effective energy resources during peak hours with the help of a home energy management system (HEMS). Although the HEMS contributes to reducing energy consumption of the building and the participation of occupants in energy saving programs, unwanted interruptions and strict guidance from the system cause inconvenience to the occupants further leading to their limited participation in the DR programs. This paper presents a human comfort-based control approach for home energy management to promote the DR participation of households. Heating and lighting systems were chosen to be controlled by human comfort factors such as thermal comfort and visual comfort. Case studies were conducted to validate the proposed approach. The results showed that the proposed approach could effectively reduce the energy consumption during the DR period and improve the occupants’ comfort.
Highlights
A building energy management system (BEMS) measures, analyzes, reports, and optimizes the demand and supply of energy in a building for saving its energy and cost, and for realizing the occupant’s comfort [1]
Simulation case studies were performed using Matrix Laboratory (MATLAB)/Simulink® to verify the effectiveness of the proposed approach
Programs, this paper has presented an algorithm based on human comfort factors that can be integrated into the home energy management system (HEMS)
Summary
A building energy management system (BEMS) measures, analyzes, reports, and optimizes the demand and supply of energy in a building for saving its energy and cost, and for realizing the occupant’s comfort [1]. As one of the methods to preserve the user’s comfort and to reduce the peak load and electricity bill at smart homes, control algorithms of home appliances, renewables, electric vehicles, and energy storage system based on the quality of experience (QoE) were presented for the HEMS [15,16] Even though these investigations did not consider the DR programs, the proposed algorithms may further cover the DR programs. Home appliances and equipment should finely be controlled by HEMS by considering the influence of the occupants’ preference and fatigue They should be considered as demand resources for the purpose of energy savings of the household during DR events. To reduce the response fatigue of occupants and to slow down the negative effect of curtailment of appliances during DR events, an HVAC system was proposed as a prior demand resource in the household due to its slower thermal dynamics and higher power capacity than those of other appliances.
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