Issue |
E3S Web Conf.
Volume 111, 2019
CLIMA 2019 Congress
|
|
---|---|---|
Article Number | 06006 | |
Number of page(s) | 7 | |
Section | Sustainable Urbanization and Energy System Integration | |
DOI | https://doi.org/10.1051/e3sconf/201911106006 | |
Published online | 13 August 2019 |
Resilient optimal design of multi-family buildings in future climate scenarios
Politecnico di Torino, DENERG, Corso Duca degli Abruzzi 24, 10129, Torino, Italy
* Corresponding author: enrico.fabrizio@polito.it
In Europe, the second recast of EPBD promotes long-term strategies to accelerate the path to nZEBs, fostering the cost-optimized building design already suggested in the EPBD first recast. Since the nZEB design is a complex optimization problem that is subjected to uncertainty in its boundary conditions (climate, technologies, market, ...), it is necessary to guarantee the resilience of the NZEB optimal design to possible variations of future scenarios, especially as regards the climate change. This work applies the new EdeSSOpt methodology (Energy Demand and Supply Simultaneous Optimization) developed by the Authors aiming at investigating the variation of the cost-optimized multi-family building design in different Italian future climate scenarios, therefore considering parameters related to the building envelope, energy systems and renewable energy sources. The method is implemented into the TRNSYS® (energy model), GenOpt (optimizer) and WeatherShift® (future climate scenario generator) tools. The resulting cost-optimal solutions in future scenarios are related to a lower global cost and a decreased total primary energy consumption. Beyond the future trends of such performance indexes, the fact that most of technical solutions associated with the optimal solutions have not changed with the studied climate scenarios, indicates a certain resilience of the optimal design variables facing climate change.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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