Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/222215
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dc.titleCOST-BENEFIT ANALYSIS (CBA) OF ENERGY RETROFIT IN SINGAPORE INSTITUTIONAL BUILDINGS USING IES-VE ENERGY SIMULATION MODEL � A CASE STUDY OF NUS SDE 2
dc.contributor.authorLEE YIH PERNG
dc.date.accessioned2017-06-09T03:07:13Z
dc.date.accessioned2022-04-22T18:00:40Z
dc.date.available2019-09-26T14:14:04Z
dc.date.available2022-04-22T18:00:40Z
dc.date.issued2017-06-09
dc.identifier.citationLEE YIH PERNG (2017-06-09). COST-BENEFIT ANALYSIS (CBA) OF ENERGY RETROFIT IN SINGAPORE INSTITUTIONAL BUILDINGS USING IES-VE ENERGY SIMULATION MODEL � A CASE STUDY OF NUS SDE 2. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/222215
dc.description.abstractTo have at least 80% of buildings certified green by year 2030, energy retrofits on existing buildings are essential. Although building energy simulation models can estimate energy savings, cost-benefit viabilities of potential retrofit alternatives on institutional buildings often remain unclear. Thus, this paper proposes a framework for optimal decision making of energy retrofit in existing institutional buildings using energy simulation model. NUS SDE 2 is selected as a case study to contextualise the analysis with IES-VE model. The framework has integrated several aspects of energy retrofit, particularly model calibration, data clearance and decision making. Calibrated with observational, experimental and energy audit data to reflect effects of occupancy and non-occupancy based changes, Mean Bias Error (MBE) and Coefficient of Variation of the Root Mean Square Error [CV (RMSE)] of the model are established as 3.942% and 8.570% respectively, which are within the most stringent values. Next, potential retrofit options and estimated energy savings are identified using hybrid approach of model simulations and heuristic method. The collected material unit cost is adjusted to project unit cost in the Singapore context depending on 3 increasing levels of retrofit with rates of 2.520, 2.828 and 3.174 respectively. Then, Cost-Benefit Analysis (CBA) is used to appraise viabilities of different retrofit options, which includes Net Present Value (NPV), Benefit Cost Ratio (BCR) and Payback Period (PP). After sensitivity analysis, decision matrix is used to compare different alternatives using multiple criteria and weightages for optimal decision making. The retrofit combination that aims to improve occupant behaviour for energy savings has topped with a score of 99.42, which is 46% better than the rest. In short, the proposed framework has achieved multiple objectives of model calibration, retrofit options identification, cost data clearance, CBA and retrofit decision optimisation. By overcoming the limitations in data validation, framework application and utility extension, future studies can focus on generalising and extending the framework to encompass more building types and locations for wider adoption.
dc.language.isoen
dc.sourcehttps://lib.sde.nus.edu.sg/dspace/handle/sde/3820
dc.subjectEnergy Retrofit
dc.subjectEnergy Simulation Model
dc.subjectFramework
dc.subjectModel Calibration
dc.subjectData Clearance
dc.subjectCost-Benefit Analysis
dc.subjectDecision Making
dc.subjectInstitutional Buildings
dc.subjectBuilding
dc.subjectPFM
dc.subjectProject and Facilities Management
dc.subjectLu Yujie
dc.subject2016/2017 PFM
dc.typeDissertation
dc.contributor.departmentBUILDING
dc.contributor.supervisorLU YUJIE
dc.description.degreeBachelor's
dc.description.degreeconferredBACHELOR OF SCIENCE (PROJECT AND FACILITIES MANAGEMENT)
dc.embargo.terms2017-06-11
Appears in Collections:Bachelor's Theses

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