{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T12:22:49Z","timestamp":1648729369637},"reference-count":18,"publisher":"Cambridge University Press (CUP)","issue":"4","license":[{"start":{"date-parts":[[2015,10,7]],"date-time":"2015-10-07T00:00:00Z","timestamp":1444176000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[2015,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper presents the development of an optimization methodology for selecting the lowest monetary cost combinations of building technologies to meet set operational energy reduction targets. The new optimization algorithm introduced in this paper departs from the notion that optimal design choices over a large set of design parameters and properties can be driven by energy targets. We assume that design parameters are determined by many concurrent considerations fighting over the attention span of the design team. Our approach starts from a design outcome and asks the question, which set of discrete technologies are the right mix to reach an energy target in the cost optimal way? Such an approach has to face the challenge that the properties of market-available building technologies have a discrete nature that makes their optimal selection a combinatorial problem. The optimization algorithm searches the discrete combinatoric space by maximizing the following objective function: calculated energy savings divided by premium cost, where cost is defined as the additional cost over a baseline solution. The algorithm is codified into a custom MATLAB script and when compared to prescriptive methodologies is shown to be more cost effective and generically applicable given a palette of building technology alternatives and their corresponding cost data.<\/jats:p>","DOI":"10.1017\/s0890060415000414","type":"journal-article","created":{"date-parts":[[2015,10,7]],"date-time":"2015-10-07T09:27:33Z","timestamp":1444210053000},"page":"417-427","source":"Crossref","is-referenced-by-count":1,"title":["Determining the cost optimum among a discrete set of building technologies to satisfy stringent energy targets"],"prefix":"10.1017","volume":"29","author":[{"given":"Brian","family":"Simmons","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias H.Y.","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.F. Jeff","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Godfried","family":"Augenbroe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2015,10,7]]},"reference":[{"key":"S0890060415000414_ref18","unstructured":"Wetter M. , & Wright J. (2003). Comparison of a generalized pattern search and genetic algorithm optimization method. Proc. 8th IBPSA Conf. ( Augenbroe G. , & Hensen J , Eds.), pp. 1401\u20131408, Eindhoven, The Netherlands, August 11\u201314."},{"key":"S0890060415000414_ref4","doi-asserted-by":"publisher","DOI":"10.1080\/0961321042000325327"},{"key":"S0890060415000414_ref2","unstructured":"Anon. (n.d.). Passivhaus. Accessed at http:\/\/passivehaus.org.uk\/ in September 2012."},{"key":"S0890060415000414_ref13","unstructured":"Lee S.H. , Fei Z. , Augenbroe G. (2011). The use of normative energy calculation beyond building performance rating systems. Proc. 12th Conf. Int. Building Performance Simulation Association, pp. 2753\u20132760, Sydney, November 14\u201316."},{"key":"S0890060415000414_ref1","unstructured":"Anon. (n.d.). Kemco. Accessed at http:\/\/www.kemco.or.kr\/\/ in September 2012."},{"key":"S0890060415000414_ref3","unstructured":"ASHRAE. (n.d.). Advanced Energy Design Guides. Atlanta, GA: American Society of Heating, and Refrigerating and Air-Conditioning Engineers, Inc. Accessed at http:\/\/www.ashrae.org\/standards-research--technology\/advanced-energy-design-guides in September 2012."},{"key":"S0890060415000414_ref6","doi-asserted-by":"publisher","DOI":"10.1145\/937503.937505"},{"key":"S0890060415000414_ref8","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475-3995.1996.tb00032.x"},{"key":"S0890060415000414_ref10","unstructured":"Heo Y. , Augenbroe G. , & Choudhary R. (2011). Risk analysis of energy-efficiency projects based on Bayesian calibration of building energy models. Proc. Building Simulation, Sydney, Australia, November 14\u201316, 2011."},{"key":"S0890060415000414_ref11","unstructured":"Kim J.-H. , Augenbroe G. , & Suh H.-S. (2012). Comparative study of the LEED and ISO-CEN building energy performance rating methods. Proc. Building Simulation, Chambery, France, August 26\u201328."},{"key":"S0890060415000414_ref12","doi-asserted-by":"publisher","DOI":"10.1127\/0941-2948\/2006\/0130"},{"key":"S0890060415000414_ref14","volume-title":"Introduction to Algorithms","author":"Leiserson","year":"2001"},{"key":"S0890060415000414_ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2013.08.061"},{"key":"S0890060415000414_ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2008.02.006"},{"key":"S0890060415000414_ref17","unstructured":"Salminen M. , Palonen M. , & Sir\u00e9n K. (2012). Combined energy simulation and multi-criteria optimisation of a LEED-certified building. Proc. 1st Building Simulation and Optimization Conf., Loughborough, UK, September 10\u201311."},{"key":"S0890060415000414_ref19","volume-title":"Experiments: Planning, Analysis, and Optimization","author":"Wu","year":"2009"},{"key":"S0890060415000414_ref7","doi-asserted-by":"publisher","DOI":"10.2172\/891598"},{"key":"S0890060415000414_ref5","volume-title":"Building Performance Simulation for Design and Operation","author":"Augenbroe","year":"2011"}],"container-title":["Artificial Intelligence for Engineering Design, Analysis and Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0890060415000414","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,20]],"date-time":"2019-04-20T03:05:21Z","timestamp":1555729521000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0890060415000414\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,10,7]]},"references-count":18,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2015,11]]}},"alternative-id":["S0890060415000414"],"URL":"https:\/\/doi.org\/10.1017\/s0890060415000414","relation":{},"ISSN":["0890-0604","1469-1760"],"issn-type":[{"value":"0890-0604","type":"print"},{"value":"1469-1760","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,10,7]]}}}