| Title |
Using Lean-and-Green Supersaturated Poly-Factorial Mini Datasets to Profile Energy Consumption Performance for an Apartment Unit |
| ID_Doc |
64877 |
| Authors |
Zarkadas, S; Besseris, G |
| Title |
Using Lean-and-Green Supersaturated Poly-Factorial Mini Datasets to Profile Energy Consumption Performance for an Apartment Unit |
| Year |
2023 |
| Published |
Processes, 11.0, 6 |
| DOI |
10.3390/pr11061825 |
| Abstract |
The Renovation Wave for Europe initiative aspires to materialize the progressive greening of 85-95% of the continental older building stock as part of the European Green Deal objectives to reduce emissions and energy use. To realistically predict the energy performance even for a single apartment building is a difficult problem. This is because an apartment unit is inherently a customized construction which is subject to year-round occupant use. We use a standardized energy consumption response approach to accelerate the setting-up of the problem in pertinent energy engineering terms. Nationally instituted Energy Performance Certification databases provide validated energy consumption information by taking into account an apartment unit's specific shell characteristics along with its installed electromechanical system configuration. Such a pre-engineered framework facilitates the effect evaluation of any proposed modifications on the energy performance of a building. Treating a vast building stock requires a mass-customization approach. Therefore, a lean-and-green, industrial-level problem-solving strategy is pursued. The TEE-KENAK Energy Certification database platform is used to parametrize a real standalone apartment. A supersaturated mini dataset was planned and collected to screen as many as 24 controlling factors, which included apartment shell layout details in association with the electromechanical systems arrangements. Main effects plots, best-subsets partial least squares, and entropic (Shannon) mutual information predictions-supplemented with optimal shrinkage estimations-formed the recommended profiler toolset. Four leading modifications were found to be statistically significant: (1) the thermal insulation of the roof, (2) the gas-sourced heating systems, (3) the automatic control category type 'A', and (4) the thermal insulation of the walls. The optimal profiling delivered an energy consumption projection of 110.4 kWh/m(2) (energy status 'B') for the apartment-an almost 20% reduction in energy consumption while also achieving upgrading from the original 'C' energy status. The proposed approach may aid energy engineers to make general empirical screening predictions in an expedient manner by simultaneously considering the apartment unit's structural configuration as well as its installed electromechanical systems arrangement. |
| Author Keywords |
energy consumption; apartment unit energy screening; supersaturated datasets; performance improvement; main effect plot; partial least squares; entropic mutual information |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001015658300001 |
| WoS Category |
Engineering, Chemical |
| Research Area |
Engineering |
| PDF |
https://www.mdpi.com/2227-9717/11/6/1825/pdf?version=1687516355
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