Multi-scale Analysis and Optimization of Building Energy Performance – Lessons Learned from Case Studies. (2015)

• Record Type: Journal Article
• Authors: Tronchin, Lamberto; Manfren, Massimiliano
• Is Part Of: Procedia engineering. Volume 118(2015)
• Page Start: 563
• Page End: 572
• Publication Date: 2015
• Subjects: Multi-scale analysis -- optimization -- building performance -- integrated design process.; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2015.08.486
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: The sustainability of the built environment largely depends on its energy and environmental performances. The overall objective, across the different phases of the building life cycle such as design phase, construction phase, commissioning phase, operation phase and eventually refurbishment phase, is to improve building and system performances in terms of economics, comfort, environmental impact and durability. Numerical simulation tools and optimization methods are needed to properly evaluate all the key performance indicators simultaneously, unveiling the existing gaps and identifying possible synergies and strategies in the performance estimation and decision-making processes for the building life cycle. Further, several modelling methodologies have been developed in order to evaluate the energy performance of buildings. Generally, every modelling methodology responds effectively to some specific tasks, but there exists a lack of integration in the overall optimization process. Given the multi-scale and multi-objective nature of the problem of optimization of the energy and environmental performances of the built environment, subject to economic and comfort constraints, an appropriate synthesis and integration process in modelling methodologies has to be identified, addressing realistically the uncertainties inherently present in every modelling strategy. Data analysis and optimization techniques are successfully used in a wide variety of applications. Although these techniques have proven to be successful in both theoretical and applied domains, questions remains about their applicability for the problems introduced before. These questions involve primarily the robustness and efficiency of solutions procedures and the ability to identify relevant properties and to deal with large quantities of data. The paper aims to analyse critically these topics by means of case studies, showing a possible path to create an integrated methodology able to synthesize all the relevant aspects previously mentioned.