Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality. (15th July 2016)

• Record Type: Journal Article
• Authors: Ascione, Fabrizio; Bianco, Nicola; De Stasio, Claudio; Mauro, Gerardo Maria; Vanoli, Giuseppe Peter
• Is Part Of: Applied energy. Volume 174(2016)
• Page Start: 37
• Page End: 68
• Publication Date: 2016-07-15
• Subjects: Building energy retrofit -- Cost-optimal analysis -- Reference building -- Pareto genetic algorithm -- Building sampling -- Hospital buildings; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.04.078
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: The energy retrofit of hospital buildings is a complex and relevant issue. A hospital reference building is developed and the cost-optimal retrofit is found. Retrofit measures are screened via preliminary sampling and sensitivity analysis. Cost-optimality is assessed by multi-stage and multi-objective Pareto optimization. Investment cost, primary energy consumption and global cost are minimized. Abstract: The 'Energy Performance of Buildings Directive' Recast (i.e., 2010/31/EU) establishes that building energy retrofit should pursue "cost-optimal levels". However, a reliable and rigorous cost-optimal analysis is an arduous and computationally-expensive issue, especially for complex buildings such as hospitals. The paper tackles this issue by providing a novel methodology to identify robust cost-optimal energy retrofit solutions. Multi-stage and multi-objective (Pareto) optimization is performed with the aim of minimizing the computational burden required to achieve reliable outcomes. The methodology combines EnergyPlus and MATLAB® and includes two optimization stages, preceded by a preliminary energy investigation that performs Latin hypercube sampling and sensitivity analysis. The preliminary investigation and the first optimization stage, which runs a genetic algorithm, aim at detecting efficient energy retrofit measures (ERMs) to reduce thermal energy demand for space heating and cooling. In the second optimization stage, these ERMs are combined with further ERMs, addressed to improve the efficiency of energy systems and to exploit renewable energy sources. Investment cost, primary energy consumption and global cost related to the resulting retrofit packages are investigated by means of smart exhaustive sampling. Finally, the cost-optimal solution is identified both in presence of a limitless economic availability and of limited budgets. The methodology is applied to a hospital reference building (RB), which represents hospitals built in South Italy between 1991 and 2005. The RB is defined by using an original approach, as required by the complexity of the examined building category. The achieved cost-optimal retrofit packages imply a reduction of primary energy consumption up to 67.9 kW h/m 2 a (12.2%) and of global cost up to 2932 k€ (24.5%) with a maximum investment of 1236 k€.