This paper describes a methodology which utilises a Reduced Order Model (ROM) in overcoming typical barriers related to the Energy Performance Contracting (EPC) such as lack of building information and uncertainty regarding post renovation. For this purpose, a ROM supported by a JModelica.org Python script (ROMFit) for the optimization and calibration process has been utilised in the Measurement and verification (M&V) process. The accuracy and benefits of this model are demonstrated by a comparison with an IES-VE Whole Building Energy Simulation Model. The results presented in this paper show that the ROM proves to be the most accurate model with regards to energy demand forecasting and furthermore, it is useful in estimating numerous energy savings of retrofitting scenarios.

Development Of A Reduced Order Model For Standard-Based Measurement And Verification To Support ECM / Piccinini, Alessandro; D'Angelo, Letizia; Seri, Federico; Deane, Conor; Sterling, Raymond; Costa, Andrea; Giretti, Alberto; Keane, Marcus M.. - ELETTRONICO. - (2019), pp. 4180-4187. (Intervento presentato al convegno 16th Conference of IBPSA tenutosi a Rome nel September 2-4th 2019).

Development Of A Reduced Order Model For Standard-Based Measurement And Verification To Support ECM

Alessandro Piccinini
;
Letizia D'Angelo;Federico Seri;Alberto Giretti;
2019-01-01

Abstract

This paper describes a methodology which utilises a Reduced Order Model (ROM) in overcoming typical barriers related to the Energy Performance Contracting (EPC) such as lack of building information and uncertainty regarding post renovation. For this purpose, a ROM supported by a JModelica.org Python script (ROMFit) for the optimization and calibration process has been utilised in the Measurement and verification (M&V) process. The accuracy and benefits of this model are demonstrated by a comparison with an IES-VE Whole Building Energy Simulation Model. The results presented in this paper show that the ROM proves to be the most accurate model with regards to energy demand forecasting and furthermore, it is useful in estimating numerous energy savings of retrofitting scenarios.
2019
978-1-7750520-1-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/277662
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