Intelligent modeling of individual thermal comfort and energy optimization
Date of Award
Doctor of Philosophy (PhD)
Electrical Engineering and Computer Science
Thermal comfort, Energy optimization
Electrical and Computer Engineering | Engineering
An intelligent model is presented in this dissertation for improving individual thermal comfort in a building at lower energy consumption than can be achieved by a conventional system. Many researchers have shown that allowing building occupants to adjust their local environments to their liking improves their satisfaction and productivity in the workplace. This idea has been called Have-It-Your-Way (HIYW) in this study. However, the concern about the possible increase of energy consumption related to the implementation of distributed environmental control systems has limited the wide adoption of the application of such systems. Conditions for thermal comfort in human occupancy of indoor environments have been defined by standards and practices. Buildings' indoor environmental control systems are designed based on these standards to meet the thermal comfort needs of about 80% of the occupants. Such One-Size-Fits-All (OSFA) systems employ only one (or a few) thermostat(s) to provide a uniform environment for the whole population.
Our approach is based on the observation that an individual has a temperature range around his or her preferred temperature value in which he or she is comfortable with the surrounding thermal environment. In this dissertation, we take advantage of this fact to optimize temperature settings within the comfort zone of all occupants with energy consumption lower than that of a traditional OSFA approach. In order to formulate this optimization problem, a static lumped parameter (resistive) building energy model, which was developed by Cosden, has been utilized, and a new measure for individual thermal comfort has been introduced, inspired by Fanger's studies. This measure, which is a departure from the population comfort model, has been utilized as a basis for the simulation of individual occupants' thermal environmental preferences.
Improved thermal comfort at lower energy consumption than is achieved by the conventional OSFA systems has been obtained through optimization in a central fashion. The optimization procedure requires a collection of all of the variables, parameters, and constraints of a system, combined into a solution to the optimization problem. The annual energy consumption has been reduced by ∼8% on average relative to the conventional systems while making all persons satisfied with their thermal environment. This is a big improvement over the current standards, whose target is to satisfy only 80% of the population.
Because of the dramatic increase in the computational complexity and time demand of optimization algorithms, they may not be practical for high-dimensional, complex applications. An intelligent model, here named Intelligent Modeling of Optimized Systems (IMOS), has been successfully developed to imitate the behavior of a large number of optimized solutions to the individual thermal comfort and energy optimization problem. Our intelligent model has reduced annual energy consumption by ∼6% relative to the conventional OSFA systems, without making anyone dissatisfied, by reducing the number of system variables in a distributed fashion. In order to predict an optimal solution to a system variable, IMOS only utilizes the variables which most directly affect that variable rather than utilizing all the variables.
With respect to the concern about increased energy consumption due to the utilization of personal environmental control, it has been shown that the thermal comfort of occupants in a building can be improved without increasing the energy expenditures--or even reducing expenditures--of indoor environmental control systems (IECS) through this optimization or introduced modeling approach.
Surface provides description only. Full text is available to ProQuest subscribers. Ask your Librarian for assistance.
Ari, Seckin, "Intelligent modeling of individual thermal comfort and energy optimization" (2009). Electrical Engineering and Computer Science - Dissertations. 17.