Genetic multi criteria optimization for existing buildings holistic retrofit

Author(s): M. Rivallain, O. Baverel, B. Peuportier
Paper category: symposium
Book title: International Symposium on Life Cycle Assessment and Construction – Civil engineering and buildings
Editor(s): A. Ventura and C. de la Roche
Print ISBN: 978-2-35158-127-8
e-ISBN: 978-2-35158-128-5
Publisher: RILEM Publications SARL
Pages: 125 – 133
Total Pages: 9
Language: English

Abstract: Building design is multi criteria. In view of the increasing environmental burdens related to the development of our modern societies, buildings environmental impacts have to be considered, at early design stage. Energy consumptions linked to existing buildings use are responsible for significant environmental impacts. Moreover, the existing stock replacement rate is inferior to 1% annually, in most of developed countries. Consequently, stock retrofit represents a major lever to reach commitments on climate change and non renewable energy consumptions mitigation. Yet, the identification of optimal sustainable retrofit programs, including actions planning, is still a difficult task for professionals.
The present paper is a contribution to decision support through optimal energy retrofit programs identification. A multi criteria genetic algorithm (NSGA-II) is used to optimize retrofit programs, on both their content and planning. The retrofit measures address building envelopes and equipments replacement. For each of these, various options are considered.
The objective functions considered target environmental impacts, financial indicators and occupants’ well-being. Solutions performances are evaluated through life cycle assessment and life cycle cost models, using dynamic thermal simulation for heating load and thermal comfort evaluation.
These methods contribute to decision support through the identification of Pareto non dominated retrofit programs, on a multi criteria basis, over life cycle.

Keywords: Building energy retrofit, Life Cycle Assessment (LCA), multi criteria optimization, genetic algorithm

Online publication: 2012
Publication Type: full_text
Public price (Euros): 0.00