Energy Retrofit Optimization by Means of Genetic Algorithms as an Answer to Fuel Poverty Mitigation in Social Housing Buildings
In accordance with national regulations, the renovation of the residential sector is an urgent task for achieving significant reductions in energy consumption and CO<sub>2</sub> emissions of the existing building stock. Social housing is particularly in need of such interventions, given...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/14/1/1 |
| _version_ | 1797946555040268288 |
|---|---|
| author | Adriana Ciardiello Jacopo Dell’Olmo Marco Ferrero Lorenzo Mario Pastore Federica Rosso Ferdinando Salata |
| author_facet | Adriana Ciardiello Jacopo Dell’Olmo Marco Ferrero Lorenzo Mario Pastore Federica Rosso Ferdinando Salata |
| author_sort | Adriana Ciardiello |
| collection | DOAJ |
| description | In accordance with national regulations, the renovation of the residential sector is an urgent task for achieving significant reductions in energy consumption and CO<sub>2</sub> emissions of the existing building stock. Social housing is particularly in need of such interventions, given the higher vulnerability of its inhabitants and its crucial role in furthering social welfare and environmental sustainability objectives. Both passive and active strategies have proved their efficacy in advancing towards these goals and also in mitigating increasing fuel poverty in low-income families. However, to optimize the best combination of such retrofit strategies, advanced optimization methodologies can be applied. Here, a multi-objective optimization methodology is implemented by a genetic algorithm (aNSGA-II) coupled to EnergyPlus dynamic energy simulations. Then, the energy consumption of the optimal solution is considered by means of EnergyPLAN simulations for the further application of active strategies. The two-step method is tested on a relevant case study, a social housing building in Rome, Italy. Results show that the applied method reduced the energy demand by 51% with passive strategies only. Active strategy implementation allowed for a further reduction of 69% in CO<sub>2</sub> emissions and 51% in energy costs. The two-step method proved effective in mitigating fuel poverty and decarbonizing the residential sector. |
| first_indexed | 2024-04-10T21:12:54Z |
| format | Article |
| id | doaj.art-3441ef46a9be439a861d8e3435cd1358 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-04-10T21:12:54Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-3441ef46a9be439a861d8e3435cd13582023-01-20T14:31:14ZengMDPI AGAtmosphere2073-44332022-12-01141110.3390/atmos14010001Energy Retrofit Optimization by Means of Genetic Algorithms as an Answer to Fuel Poverty Mitigation in Social Housing BuildingsAdriana Ciardiello0Jacopo Dell’Olmo1Marco Ferrero2Lorenzo Mario Pastore3Federica Rosso4Ferdinando Salata5DICEA, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, ItalyDIAEE, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, ItalyDICEA, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, ItalyDIAEE, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, ItalyDICEA, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, ItalyDIAEE, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, ItalyIn accordance with national regulations, the renovation of the residential sector is an urgent task for achieving significant reductions in energy consumption and CO<sub>2</sub> emissions of the existing building stock. Social housing is particularly in need of such interventions, given the higher vulnerability of its inhabitants and its crucial role in furthering social welfare and environmental sustainability objectives. Both passive and active strategies have proved their efficacy in advancing towards these goals and also in mitigating increasing fuel poverty in low-income families. However, to optimize the best combination of such retrofit strategies, advanced optimization methodologies can be applied. Here, a multi-objective optimization methodology is implemented by a genetic algorithm (aNSGA-II) coupled to EnergyPlus dynamic energy simulations. Then, the energy consumption of the optimal solution is considered by means of EnergyPLAN simulations for the further application of active strategies. The two-step method is tested on a relevant case study, a social housing building in Rome, Italy. Results show that the applied method reduced the energy demand by 51% with passive strategies only. Active strategy implementation allowed for a further reduction of 69% in CO<sub>2</sub> emissions and 51% in energy costs. The two-step method proved effective in mitigating fuel poverty and decarbonizing the residential sector.https://www.mdpi.com/2073-4433/14/1/1multi-objective optimizationfuel povertygenetic algorithmsocial housingretrofitpassive strategies |
| spellingShingle | Adriana Ciardiello Jacopo Dell’Olmo Marco Ferrero Lorenzo Mario Pastore Federica Rosso Ferdinando Salata Energy Retrofit Optimization by Means of Genetic Algorithms as an Answer to Fuel Poverty Mitigation in Social Housing Buildings Atmosphere multi-objective optimization fuel poverty genetic algorithm social housing retrofit passive strategies |
| title | Energy Retrofit Optimization by Means of Genetic Algorithms as an Answer to Fuel Poverty Mitigation in Social Housing Buildings |
| title_full | Energy Retrofit Optimization by Means of Genetic Algorithms as an Answer to Fuel Poverty Mitigation in Social Housing Buildings |
| title_fullStr | Energy Retrofit Optimization by Means of Genetic Algorithms as an Answer to Fuel Poverty Mitigation in Social Housing Buildings |
| title_full_unstemmed | Energy Retrofit Optimization by Means of Genetic Algorithms as an Answer to Fuel Poverty Mitigation in Social Housing Buildings |
| title_short | Energy Retrofit Optimization by Means of Genetic Algorithms as an Answer to Fuel Poverty Mitigation in Social Housing Buildings |
| title_sort | energy retrofit optimization by means of genetic algorithms as an answer to fuel poverty mitigation in social housing buildings |
| topic | multi-objective optimization fuel poverty genetic algorithm social housing retrofit passive strategies |
| url | https://www.mdpi.com/2073-4433/14/1/1 |
| work_keys_str_mv | AT adrianaciardiello energyretrofitoptimizationbymeansofgeneticalgorithmsasananswertofuelpovertymitigationinsocialhousingbuildings AT jacopodellolmo energyretrofitoptimizationbymeansofgeneticalgorithmsasananswertofuelpovertymitigationinsocialhousingbuildings AT marcoferrero energyretrofitoptimizationbymeansofgeneticalgorithmsasananswertofuelpovertymitigationinsocialhousingbuildings AT lorenzomariopastore energyretrofitoptimizationbymeansofgeneticalgorithmsasananswertofuelpovertymitigationinsocialhousingbuildings AT federicarosso energyretrofitoptimizationbymeansofgeneticalgorithmsasananswertofuelpovertymitigationinsocialhousingbuildings AT ferdinandosalata energyretrofitoptimizationbymeansofgeneticalgorithmsasananswertofuelpovertymitigationinsocialhousingbuildings |