Exergy As a Measure of Sustainable Retrofitting of Buildings

Abstract

This study presents a novel optimization methodology for choosing optimal building retrofitting strategies based on the concept of exergy analysis. The study demonstrates that the building exergy analysis may open new opportunities in the design of an optimal retrofit solution despite being a theoretical approach based on the high performance of a Carnot reverse cycle. This exergy-based solution is different from the one selected through traditional efficient retrofits where minimizing energy consumption is the primary selection criteria. The new solution connects the building with the reference environment, which acts as "an unlimited sink or unlimited sources of energy", and it adapts the building to maximize the intake of energy resources from the reference environment. The building hosting the School of Architecture at the University of Navarra has been chosen as the case study building. The unique architectural appearance and bespoke architectural characteristics of the building limit the choices of retrofitting solutions; therefore, retrofitting solutions on the façade, roof, roof skylight and windows are considered in multi-objective optimization using the jEPlus package. It is remarkable that different retrofitting solutions have been obtained for energy-driven and exergy-driven optimization, respectively. Considering the local contexts and all possible reference environments for the building, three "unlimited sinks or unlimited sources of energy" are selected for the case study building to explore exergy-driven optimization: the external air, the ground in the surrounding area and the nearby river. The evidence shows that no matter which reference environment is chosen, an identical envelope retrofitting solution has been obtained.

Evaluating and Enhancing the Energy Efficiency of Representative Residential Buildings by Applying National and International Standards Using BIM

“…This target does not seem applicable to most buildings in many countries and regions, which do not even meet the building energy codes. Other papers [29][30][31][32][33][34] chose more representative case studies through extensive criteria of choice related to building and climate characteristics. Nonetheless, Attia's work [35] developed representative building energy datasets and benchmark simulation models for the Egyptian residential sector.…”

A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency

“…While some default parameters may adequately fit a range of MOO retrofit problems, such as the crossover and mutation ones, the more tailored to the problems' specificities and well designed the GA input parameters are, the more efficient and correctly implemented will the GA-based MOO be. For setting these parameters and stopping criteria, some PS [20,105,111,123,130,132,144,157,161] have chosen values based on expertise and best practice, as well as those leading to the best trade-off between computational burden and proven reliability of the Pareto front through their own work or previous literature, or values according to the software recommended default parameters. The design of these parameters directly affects GA's performance, convergence rate, the accuracy of the optimal solutions achieved and the computational burden.…”

“…jEPlus+EA, an optimisation engine oriented tool, takes second place within the most used optimisation tools after MATLAB [11,22,71,88,135,145,147,156,157]. It couples jEPlus, the Java shell to perform parametric analysis for EnergyPlus, with a modified NSGA [180].…”

“…It encompasses firstly window options (number of layers, low emissivity coating option, void gas type, frame type), which is found to attract the most research attention[10-12,20,22,69,71,76,87,105- 107,109-111,117,123,127,128,132-134,138-143,145,147,148,150-156,158-161,163]. Additionally, other variables related to window thermal performance are considered for optimisation: total solar energy transmittance (g-value), heat transfer coefficient (U-value)[22,109,138,150,157,159,160,162] and window-towall ratio[11,20,76,122,141,143,145,146,154].The second most frequent variables are linked to the external walls and roof thermal transmittance (U-value), also presented as insulation thickness[10,11,22,69,71,76,105- 107,109,111,117,123,127,132,134,135,138,141,145,147,148,150-154,156-163]. Ground floor, ceiling and internal partitions insulation are analysed as well[10,11,71,117,127,148,151,156,160].…”

Empirical and Comparative Validation for a Building Energy Model Calibration Methodology

“…Energy simulation has become a powerful tool supporting the design of new buildings and in proposing Energy Conservation Measures (ECM) in existing buildings. It is used in the application of the predictive model control (MPC) techniques [ 4 , 5 , 6 , 7 , 8 , 9 ], in actions for the operational optimization of Heating Ventilation Air Conditioning (HVAC) [ 10 , 11 ], in economic strategies [ 12 , 13 , 14 , 15 ], and in the optimization of cost-effective building refurbishment [ 16 , 17 ]. In terms of building energy models (BEMs), Hence and Lamberts [ 18 ] differentiated between the following types depending on the physical relevance of the parameters: White box models are based on a physical models with exclusively physically meaningful parameters.…”