Abstract

Homes that utilise solar thermal and solar photovoltaic (PV) technologies to generate as much energy as they consume in a year are referred to as net-Zero Energy Solar Homes (ZESH). This thesis presents the methodology used to develop a Genetic Algorithm (GA) Optimisation Tool that finds optimal configurations of low and net-zero energy solar homes taking into consideration effects from the use of different technologies, local climate, economics, and other factors. The tool links a low energy solar home model developed in TRNSYS with a GA optimisation program that automates the search for cost-effective building designs. The tool varies a predefined set of parameters including building width to length ratio, heating system type, solar thermal collector type and size, and more. The results from the TRNSYS model are compared with monitored data of an energy efficient house to verify that the model was correctly implemented. A total of 40 test cases were evaluated with the tool in order to verify the effects of climate, energy consumption target, control strategy, utility price structures, and other factors, to examine their impact on the resulting optimal design configurations. The GA program was capable of finding designs that were on average within 0.5% of the best known solution with the evaluation of only 0.00012% of the solution space. Results indicated that homes could be built with near equivalent monthly costs of conventional homes, while reducing the annual net-energy consumption by an order of magnitude. A reduction in PV system costs or the introduction of appropriate feed-in-tariffs had significant impacts on the overall cost-effectiveness of ZESH. The thesis clearly demonstrated the extent to which local climate, economic factors, and specific design constraints can have a major impact on the optimal design configuration, which limits the usefulness of generic design guidelines. The methodology developed was also a novel way of using TRNSYS for the optimisation of whole building design through the use in updated input and parameter files. The development of building design optimisation tools will lead to various applications ranging from policy development work where the benefits of different incentive mechanisms are quantified, to design assistance, and building code development work.