The new European targets of achieving net zero emissions by 2050 have spurred Italy to aim for a 30% reduction in emissions by 2030, compared with 2005 levels. This goal will be achieved through the promotion of renewable energy sources and energy savings in the residential sector, which remains one of the main sectors accountable for total energy consumption, mainly for heating. This study aims at investigating the potential of some retrofit measures implemented in the Umbria Region, chosen as a case study, to reach the goal by 2030. Using parametric energy simulations with the standard calculation method and artificial neural networks (ANN), the energy consumption of Umbria’s building stock and potential CO2 reductions were assessed. Results showed that with current energy policies, a reduction of 28% could be achieved, which is below the goal by 2030, while ANN integration within energy strategies could allow reaching it as early as 2025 or 2029, depending on the restriction set to the ANN and the extent of current energy policies. This study confirmed the potential benefits of using advanced technology in achieving national environmental goals, highlighting that they could be essential tools to be integrated into energy policies to accelerate progress towards ambitious climate goals
Greening Umbria’s Future: Investigation of the Retrofit Measures’ Potential to Achieve Energy Goals by 2030 in the Umbria Region
Palladino D.
2023-01-01
Abstract
The new European targets of achieving net zero emissions by 2050 have spurred Italy to aim for a 30% reduction in emissions by 2030, compared with 2005 levels. This goal will be achieved through the promotion of renewable energy sources and energy savings in the residential sector, which remains one of the main sectors accountable for total energy consumption, mainly for heating. This study aims at investigating the potential of some retrofit measures implemented in the Umbria Region, chosen as a case study, to reach the goal by 2030. Using parametric energy simulations with the standard calculation method and artificial neural networks (ANN), the energy consumption of Umbria’s building stock and potential CO2 reductions were assessed. Results showed that with current energy policies, a reduction of 28% could be achieved, which is below the goal by 2030, while ANN integration within energy strategies could allow reaching it as early as 2025 or 2029, depending on the restriction set to the ANN and the extent of current energy policies. This study confirmed the potential benefits of using advanced technology in achieving national environmental goals, highlighting that they could be essential tools to be integrated into energy policies to accelerate progress towards ambitious climate goalsFile | Dimensione | Formato | |
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