Institutional Repository
Technical University of Crete
EN  |  EL

Search

Browse

My Space

OptEEmAL: decision-support tool for the design of energy retrofitting projects at district level

García Fuentes, Miguel, Hernández Gema, Serna Víctor, Martín Susana, Álvarez Sonia, Lilis Georgios-Nektarios, Giannakis Georgios, Katsigarakis Kyriakos, Mabe Lara, Oregi Xabat, Manjarrés Diana, Ridouane Hassan El, De Tommasi Luciano

Full record


URI: http://purl.tuc.gr/dl/dias/640D8B73-3EBD-4B5B-9084-F976B38CACC0
Year 2019
Type of Item Conference Full Paper
License
Details
Bibliographic Citation M.A. Garciá-Fuentes, G. Hernández, V. Serna, S. Martín, S. Álvarez, G.N. Lilis, G. Giannakis, K. Katsigarakis, L. Mabe, X. Oregi, D. Manjarres, H.E. Ridouane and L. De Tommasi, "OptEEmAL: decision-support tool for the design of energy retrofitting projects at district level," in Central Europe towards Sustainable Building, 2019. doi: 10.1088/1755-1315/290/1/012129 https://doi.org/10.1088/1755-1315/290/1/012129
Appears in Collections

Summary

Designing energy retrofitting actions poses an elevated number of problems, as the definition of the baseline, selection of indicators to measure performance, modelling, setting objectives, etc. This is time-consuming and it can result in a number of inaccuracies, leading to inadequate decisions. While these problems are present at building level, they are multiplied at district level, where there are complex interactions to analyse, simulate and improve. OptEEmAL proposes a solution as a decision-support tool for the design of energy retrofitting projects at district level. Based on specific input data (IFC(s), CityGML, etc.), the platform will automatically simulate the baseline scenario and launch an optimisation process where a series of Energy Conservation Measures (ECMs) will be applied to this scenario. Its performance will be evaluated through a holistic set of indicators to obtain the best combination of ECMs that complies with user's objectives. A great reduction in time and higher accuracy in the models are experienced, since they are automatically created and checked. A subjective problem is transformed into a mathematical problem; it simplifies it and ensures a more robust decision-making. This paper will present a case where the platform has been tested.

Available Files

Services

Statistics