A metabolic network approach to building performance: Information building modeling and simulation of biological indicators. (1st November 2017)

• Record Type: Journal Article
• Authors: Yi, Hwang; Braham, William W.; Tilley, David R.; Srinivasan, Ravi
• Is Part Of: Journal of cleaner production. Volume 165(2017)
• Page Start: 1133
• Page End: 1162
• Publication Date: 2017-11-01
• Subjects: Building performance -- Information -- Metabolic network -- Emergy -- Net-zero energy building; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2017.07.082
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: In an effort to complement energy-efficiency oriented methods of environmental building study, this research seeks to understand building as a metabolic thermodynamic network, and investigates performance and sustainability with system-level principles and biological indicators. This approach introduces two system evaluation metrics: (i) solar emjoule (sej) of emergy (spelled with an "m") and (ii) bits of information. In this way, emergy-information indicates the intensive organization of building energy flux in association with global environment development. Findings from a comparative analysis of a net-zero energy building (NZEB) and a non-NZEB show that buildings increase network complexity, resilience, and adaptability as they reduce nonrenewable inputs with energy feedback loops. The consistent tendency of maximizing information and complexity at the expense of efficiency demonstrates that building thermodynamic networking parallels the general ecosystems developmental phenomenon –maximum power at optimal efficiency. Highlights: A network-based methodology is suggested to model building energy metabolism. Emergy-information integrated indices are applied to net-zero energy building performance. Sustainable buildings tend to compromise efficiency to increase power, information, and complexity.