A novel approach to building performance optimization via iterative operations on attribute clusters of design options

Abstract

[no german abstract available] The present paper explores the potential of a novel approach toward iterative global optimization of locally optimized attribute clusters of building design solutions. Thereby, clusters of design space attributes that are comprehensible to typical building designers as a compound yet coherent aspects of a design are made subject to multiple passes of local simulation-assisted optimizations. Hence, instead of allocating an individual dimension to each and every variable of a complex design within the context of a single-pass global optimization campaign, multiple iterative optimization steps target coherent clusters of such attributes and pursue those until the overall design meets the expected performance (or until further performance improvement is not forthcoming). The implementation of the proposed approach employs a number of existing - and freely accessible - computational applications, including an optimization software coupled to an energy simulation tool. We illustrate and document the performance of the current implementation of the proposed approach via an optimization case study. Thereby, different system operation options (i.e., sequential versus random cycling between attribute clusters) are demonstrated.

The present paper explores the potential of a novel approach toward iterative global optimization of locally optimized attribute clusters of building design solutions. Thereby, clusters of design space attributes that are comprehensible to typical building designers as a compound yet coherent aspects of a design are made subject to multiple passes of local simulation-assisted optimizations. Hence, instead of allocating an individual dimension to each and every variable of a complex design within the context of a single-pass global optimization campaign, multiple iterative optimization steps target coherent clusters of such attributes and pursue those until the overall design meets the expected performance (or until further performance improvement is not forthcoming). The implementation of the proposed approach employs a number of existing - and freely accessible - computational applications, including an optimization software coupled to an energy simulation tool. We illustrate and document the performance of the current implementation of the proposed approach via an optimization case study. Thereby, different system operation options (i.e., sequential versus random cycling between attribute clusters) are demonstrated.