000220873 001__ 220873
000220873 005__ 20181205220223.0
000220873 0247_ $$2doi$$a10.5075/epfl-thesis-7058
000220873 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis7058-2
000220873 02471 $$2nebis$$a10694500
000220873 037__ $$aTHESIS
000220873 041__ $$aeng
000220873 088__ $$a7058
000220873 245__ $$aSolar potential in early neighborhood design$$ba decision-support workflow based on predictive models
000220873 260__ $$aLausanne$$bEPFL$$c2016
000220873 269__ $$a2016
000220873 300__ $$a308
000220873 336__ $$aTheses
000220873 502__ $$aProf. Michel Bierlaire (président) ; Prof. Marilyne Andersen, Prof. Emmanuel Rey (directeurs) ; Prof. Bernard Cache, Prof. Raphaël Compagnon, Prof. Koen Steemers (rapporteurs)
000220873 520__ $$aIn light of the acknowledged need for a transition toward sustainable cities, neighborhoods and buildings, urban planners, architects and engineers have to comply with evermore demanding energy regulations. These decision-makers must be supported early-on in their process by adequate methods and tools. Indeed, early-design decisions, which concern parameters linked to the building form and urban layout, strongly dictate the solar exposure levels of buildings, in turn influencing their energy need (e.g. for heating and cooling) and production potential (e.g. through on-site active solar systems). Despite the spread of existing digital tools, limitations remain, withholding their integration into the early design process. These considerations lay down the context within which this doctoral research was carried out. The main objective of this thesis is the development of a performance-based workflow to support decision-making in early-design neighborhood projects. The performance is here defined through three criteria: (i) the daylight potential, quantified by the spatial daylight autonomy, (ii) the passive solar potential, quantified by the annual energy need for space heating and cooling, and (iii) the active solar potential, quantified by the annual energy production. The research process consisted of two main phases. First, the development of a performance assessment engine allowing real-time evaluation of an ensemble of buildings. Second, the integration of this method into a decision-support workflow, taking the form of a digital prototype that was tested among practitioners. For the first phase, a metamodeling approach was adopted to circumvent the limitations associated to simulations involving solving physics-based equations. Mathematical functions were obtained to predict the daylight and energy performance of a neighborhood, from a series of geometry- and irradiation-based parameters, easily computable at the early-design phase. To derive these functions (or metamodels), a neighborhood modeling and simulation procedure was executed to acquire a dataset of reference cases, from which the metamodels were trained and tested. The resulting multiple-linear regression functions, combined to an algorithm for quantifying the active solar potential from the irradiation data, formed our performance assessment engine. To assess its usability and relevance, the workflow was implemented as a prototype, supported by existing 3D modeling and scripting tools. Inspired by the emerging performance-driven and non-linear design paradigms, a multi-variant approach was adopted for this implementation; from the space of possible designs defined by a small set of user-inputs, a series of neighborhood variants are generated through a random sampling algorithm. Results of their evaluation by the core engine are displayed to allow a comparative assessment of the variants in terms of their morphology and solar potential. Having been tested among practitioners during workshops, the prototype appears promising for providing design decision-support. Direct feedback gathered from participants support the relevance of the approach and reveals multiple avenues for further improvement. Results collected during the workshops also allowed probing the validity boundaries of the metamodels: the prediction accuracy achieved attests the potential of the approach as an alternative to more complex methods, less adequate for exploring early-phase design alternatives.
000220873 6531_ $$aearly-phase neighborhood design
000220873 6531_ $$apassive and active solar potential
000220873 6531_ $$adesign decision-support
000220873 6531_ $$apredictive mathematical model
000220873 6531_ $$amulti-criteria performance assessment
000220873 6531_ $$aparametric modeling
000220873 6531_ $$aenergy and daylight simulation
000220873 700__ $$0245834$$aNault, Émilie$$g211592
000220873 720_2 $$0241777$$aAndersen, Marilyne$$edir.$$g103938
000220873 720_2 $$0244344$$aRey, Emmanuel$$edir.$$g103324
000220873 8564_ $$s54091626$$uhttps://infoscience.epfl.ch/record/220873/files/EPFL_TH7058.pdf$$yn/a$$zn/a
000220873 8564_ $$s296888$$uhttps://infoscience.epfl.ch/record/220873/files/EPFL_TH7058_THUMB_1.png$$yn/a$$zn/a
000220873 909C0 $$0252313$$pLIPID$$xU12325
000220873 909C0 $$0252299$$pLAST$$xU12329
000220873 909CO $$ooai:infoscience.tind.io:220873$$pthesis$$pthesis-bn2018$$pDOI$$pENAC$$qDOI2
000220873 917Z8 $$x108898
000220873 917Z8 $$x108898
000220873 917Z8 $$x252028
000220873 917Z8 $$x108898
000220873 917Z8 $$x108898
000220873 917Z8 $$x108898
000220873 918__ $$aENAC$$cIA$$dEDCE
000220873 919__ $$aLIPID
000220873 920__ $$a2016-8-24$$b2016
000220873 970__ $$a7058/THESES
000220873 973__ $$aEPFL$$sPUBLISHED
000220873 980__ $$aTHESIS