Using Thermostats for Indoor Climate Control in Office Buildings: The Effect on Thermal Comfort

Abstract

Thermostats are widely used in temperature regulation of indoor spaces and have a direct impact on energy use and occupant thermal comfort. Existing guidelines make recommendations for properly selecting set points to reduce energy use, but there is little or no information regarding the actual achieved thermal comfort of the occupants. While dry-bulb air temperature measured at the thermostat location is sometimes a good proxy, there is less understanding of whether thermal comfort targets are actually met. In this direction, we have defined an experimental simulation protocol involving two office buildings; the buildings have contrasting geometrical and construction characteristics, as well as different building services systems for meeting heating and cooling demands. A parametric analysis is performed for combinations of controlled variables and boundary conditions. In all cases, occupant thermal comfort is estimated using the Fanger index, as defined in ISO 7730. The results of the parametric study suggest that simple bounds on the dry-bulb air temperature are not sufficient to ensure comfort, and in many cases, more detailed considerations taking into account building characteristics, as well as the types of building heating and cooling services are required. The implication is that the calculation or estimation of detailed comfort indices, or even the use of personalised comfort models, is key towards a more human-centric approach to building design and operation.

Model-Based Monitoring of Occupant’s Thermal State for Adaptive HVAC Predictive Controlling

“…In modern buildings, it is very common that HVAC control systems are designed in such way to ensure parsimonious energy use and cost-effective building operation. This often happens by tuning HVAC control parameters (e.g., set points) to exploit the inherent trade-off between energy consumption and thermal comfort, with the latter acting as a constraint defining a theoretical and practical upper bound on potential energy savings [28][29][30]. In this paper, we suggested a model-predictive control (MPC) strategy, which is based on continuous feedback of occupant's thermal state (sensation/comfort) with main control objective to achieve occupant's thermal comfort.…”

Effectiveness enhancement and performance evaluation of indirect‐direct evaporative cooling system for a wide variety of climates

“…Estimation of indoor thermal comfort has been developed by wide‐ranging of methodologies . The most widely accepted models according to their adoption in indoor climate thermal comfort standards are categorized into three models: Fanger's predictive mean vote (PMV) model which is used in the American Society of Heating, Refrigerating and Air‐Conditioning Engineers (ASHRAE 55) and the International Organization for Standardization (ISO 7730) standards. The Adaptive Comfort Model of the ASHRAE Standard 55‐2010 . The Adaptive Comfort Model of the European Standard EN 15251 . …”

“…Estimation of indoor thermal comfort has been developed by wide-ranging of methodologies [17]. The most widely accepted models according to their adoption in indoor climate thermal comfort standards are categorized into three models: [21].…”

Simulation-Based Evaluation and Optimization of Control Strategies in Buildings

“…Another interesting observation is that there is not much variability to the control setpoints of the best control solution designed by the GP_SS approach. This is because the cooling system is air-based and capable of covering the cooling demand, so there are no time-delay effects, as we see in buildings equipped with systems such as radiators [4,25] or Thermally Activated Building Slabs (TABS) [2,25]. This would imply that only for this HVAC system a more targeted setpoint/control parameter search for the MCDA approach could lead to the same results as the fully-fledged optimisation methodology followed in GP_SS approach.…”

“…In addition, as analysed in [2,25], there is a cost (e.g., express as productivity loss [26,27]) associated with the inability of most MPC-oriented bi-linear models to accurately estimate occupant thermal sensation, e.g., under the guidelines of ISO 7730 [28] or ASHRAE 55 [29], as most MPC applications in buildings define room thermal comfort as a pre-defined region of air [30] or operative [3] temperatures with only few exceptions [31][32][33][34]. This limitation becomes more restricting as personalised thermal comfort models [35][36][37][38][39] have increasingly started becoming the norm.…”