Optimization of energy efficient housing for the lower income demographic utilizing a generalized pattern search particle swarm optimization algorithm
Abstract
The optimization of energy efficient housing is a highly complex problem involving hundreds of parameters and many end objectives that results in at least billions upon billions of possible outcomes. This research sets forth to take a small portion of the possible design space for a low income targeted, residential home and perform a optimization by utilizing particle swarm optimization algorithms in conjunction with complex energy modeling software to develop a set of Pareto front solutions based on actual regional material cost and energy data. This study intends to capture these effects in multiple climate locations to show climatic trends and effects on building optimization results and give home builders and non profit organizations a tool and information they need to better serve their community. The data will show that clear Pareto curves develop showing a strong set of optimal results even when such a small portion of the design space is considered. The data also shows strong climate trends which bring to light what design solutions are the best in each type of location.
Degree
M.S.C.E.
Advisors
Horton, Purdue University.
Subject Area
Architectural|Civil engineering|Energy
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