Retrofit of villas on Mediterranean coastlines: Pareto optimization with a view to energy-efficiency and cost-effectiveness

Abstract A comprehensive multi-objective optimization framework is proposed to address the energy retrofit of typical villas on Mediterranean coastlines. Primary energy consumption and global cost are minimized using a Pareto approach. Two different construction technologies (i.e., a lightweight house in reinforced concrete and a massive tuff-made villa), in two different climates (i.e., Greek and Italian coasts), are investigated to provide robust optimal retrofit strategies according to two different criteria, i.e., the achievement of the nearly zero energy standard and the cost-optimality, respectively. The optimal energy retrofit strategies are achieved by coupling transient energy simulations and a genetic algorithm. They address all main factors affecting energy performance, i.e., building envelope (thermal insulation, reflectance of coatings, windows and solar shading), active energy systems (space conditioning devices) and renewable energy sources (solar photovoltaics). The investigation findings can provide useful generic guidelines for the retrofit of houses on the Mediterranean coastlines with a view to energy-efficiency and cost-effectiveness. The implementation of the suggested retrofit strategies to the case studies can yield substantial primary energy savings, up to 125 kWh/m2a, and global cost savings, up to 140 €/m2. The most energy-efficient and cost-effective retrofit solutions are the improvement of energy systems’ efficiency, the installation of full-roof photovoltaic systems, the replacement of windows and the roof thermal insulation.

[1]  Birgit Dagrun Risholt,et al.  Sustainability assessment of nearly zero energy renovation of dwellings based on energy, economy and home quality indicators , 2013 .

[2]  Marta Molina Huelva,et al.  Passive actions in the building envelope to enhance sustainability of schools in a Mediterranean climate , 2019, Energy.

[3]  Jan Hensen,et al.  A new methodology for investigating the cost-optimality of energy retrofitting a building category , 2015 .

[4]  R. Margolis,et al.  Solar plus: Optimization of distributed solar PV through battery storage and dispatchable load in residential buildings , 2018 .

[5]  Enrico Fabrizio,et al.  EDeSSOpt – Energy Demand and Supply Simultaneous Optimization for cost-optimized design: Application to a multi-family building , 2019, Applied Energy.

[6]  Ralph Evins,et al.  A review of computational optimisation methods applied to sustainable building design , 2013 .

[7]  M. Hamdy,et al.  A multi-stage optimization method for cost-optimal and nearly-zero-energy building solutions in line with the EPBD-recast 2010 , 2013 .

[8]  Aristides Kiprakis,et al.  A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies , 2015 .

[9]  Basak Gucyeter,et al.  Optimization of an envelope retrofit strategy for an existing office building , 2012 .

[10]  C. Cartalis,et al.  On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings—A review , 2015 .

[11]  Alfonso Aranda-Usón,et al.  Analysis of energy poverty intensity from the perspective of the regional administration: Empirical evidence from households in southern Europe , 2015 .

[12]  Ala Hasan,et al.  Applying a multi-objective optimization approach for Design of low-emission cost-effective dwellings , 2011 .

[13]  Long Zhang,et al.  Performance analysis and multi-objective optimization of a hybrid photovoltaic/thermal collector for domestic hot water application , 2018 .

[14]  Dongmei Pan,et al.  Comparative studies on using RSM and TOPSIS methods to optimize residential air conditioning systems , 2018 .

[15]  Xiaohua Xia,et al.  A Multi-objective Optimization Model for Building Envelope Retrofit Planning☆ , 2015 .

[16]  Paul Ruyssevelt,et al.  ExRET-Opt: An automated exergy/exergoeconomic simulation framework for building energy retrofit analysis and design optimisation , 2017 .

[17]  Yang Zhao,et al.  Renewable energy system optimization of low/zero energy buildings using single-objective and multi-objective optimization methods , 2015 .

[18]  Gianluca Rapone,et al.  Optimisation of curtain wall façades for office buildings by means of PSO algorithm , 2012 .

[19]  Marco Perino,et al.  Optimisation analysis of PCM-enhanced opaque building envelope components for the energy retrofitting of office buildings in Mediterranean climates , 2018 .

[20]  Gerardo Maria Mauro,et al.  Building envelope design: Multi-objective optimization to minimize energy consumption, global cost and thermal discomfort. Application to different Italian climatic zones , 2019, Energy.

[21]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[22]  Tomas Ekström,et al.  Cost-effective Passive House renovation packages for Swedish single-family houses from the 1960s and 1970s , 2018 .

[23]  Jessica Granderson,et al.  Field evaluation of performance of HVAC optimization system in commercial buildings , 2018, Energy and Buildings.

[24]  Germán Ramos Ruiz,et al.  Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model , 2018, Energies.

[25]  Jan Carmeliet,et al.  Multiobjective optimisation of energy systems and building envelope retrofit in a residential community , 2017 .

[26]  Gerardo Maria Mauro,et al.  A Multi-Criteria Approach to Achieve Constrained Cost-Optimal Energy Retrofits of Buildings by Mitigating Climate Change and Urban Overheating , 2018 .

[27]  Gerardo Maria Mauro,et al.  A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance , 2015 .

[28]  M. Santamouris Cooling the cities – A review of reflective and green roof mitigation technologies to fight heat island and improve comfort in urban environments , 2014 .

[29]  Gerardo Maria Mauro,et al.  Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality , 2016 .

[30]  F. Ascione Energy conservation and renewable technologies for buildings to face the impact of the climate change and minimize the use of cooling , 2017 .

[31]  Enrico Fabrizio,et al.  Cost-Optimal Analysis for Nearly Zero Energy Buildings Design and Optimization: A Critical Review , 2018, Energies.

[32]  Jean-Louis Scartezzini,et al.  Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand , 2017, Energy and Buildings.

[33]  Alessandro Prada,et al.  On the performance of meta-models in building design optimization , 2018 .

[34]  Luis C. Dias,et al.  Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application , 2014 .

[35]  Mahdi Shahbakhti,et al.  Optimal exergy control of building HVAC system , 2015 .

[36]  Xiaohua Xia,et al.  A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with rooftop PV system installation and maintenance , 2017 .

[37]  Jeong Tai Kim,et al.  Optimization of the building integrated photovoltaic system in office buildings—Focus on the orientation, inclined angle and installed area , 2012 .

[38]  Philippe Rigo,et al.  A review on simulation-based optimization methods applied to building performance analysis , 2014 .

[39]  Elias Kyriakides,et al.  Energy scheduling in non-residential buildings integrating battery storage and renewable solutions , 2018, 2018 IEEE International Energy Conference (ENERGYCON).

[40]  Vandana Sehgal,et al.  Finding the gaps and methodology of passive features of building envelope optimization and its requirement for office buildings in India , 2019, Thermal Science and Engineering Progress.

[41]  M. Assimakopoulos,et al.  On the relation between the energy and social characteristics of the residential sector , 2007 .

[42]  P. Popovski,et al.  Reducing the carbon footprint of house heating through model predictive control – A simulation study in Danish conditions , 2018, Sustainable Cities and Society.

[43]  Elisa Sirombo,et al.  Energy-optimized versus cost-optimized design of high-performing dwellings: The case of multifamily buildings , 2018 .