A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin

Fabrizio Ascione, Nicola Bianco, Gerardo Maria Mauro and Giuseppe Peter Vanoli

Applied Energy, 2019, vol. 241, issue C, 361 pages

Abstract: The comprehensive optimization of building energy design is fundamental to promote sustainability but it is an arduous issue that involves a huge domain of variables and objectives. The proposed investigation addresses this issue through a novel comprehensive framework – Harlequin – that performs a multi-phase and multi-objective design optimization. Three phases are carried out to optimize design variables related to the whole building-plants system, considering different energy, comfort, economic and environmental performance indicators. Phase 1 implements a genetic algorithm to achieve the Pareto optimization of envelope, geometry and space conditioning set points. Phase 2 performs a smart exhaustive sampling of design scenarios to find optimal energy systems. Phase 3 provides the most sustainable, the cost-optimal and the lowest investment (but energy-efficient) design solutions. Among these, the stakeholders can choose the best solution according to their wills and needs. Harlequin uses EnergyPlus (only in phase 1) and MATLAB® and it is so-called because building geometry and envelope are optimized for each exposure, thereby providing “Harlequin buildings”. The novelty and scientific significance consist in ensuring a reliable design optimization by investigating a domain of variables and objectives, as comprehensive as never before. As a case study, Harlequin is applied to design a typical Italian office in Milan. Compared to a reference design, significant reductions of primary energy consumption (PEC), global cost (GC) and CO2-eq emissions can be achieved, depending on the chosen solution. The maximum reductions are 43.9 kWhp/m2 a for PEC, 63.9 €/m2 for GC (discount rate of 3%) and 12.3 kg/m2 a for CO2-eq.

Keywords: Building design; Energy efficiency; Building energy simulation; Building energy optimization; Multi-objective genetic algorithm; Cost-optimal analysis (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (21)

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DOI: 10.1016/j.apenergy.2019.03.028

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