A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Closing the energy flexibility gap: Enriching flexibility performance rating of buildings with monitored data
https://orcid.org/0000-0002-3735-6006
https://orcid.org/0000-0002-8771-3489
https://orcid.org/0000-0003-0357-2090
https://orcid.org/0000-0003-3466-7850
https://orcid.org/0000-0001-6352-2226
Highlights Energy Flexibility Gap – difference between expected and actual energy flexibility. We look at the part of the Energy Flexibility Gap related to operational factors. Building energy flexibility ratings can be made more realistic with monitored data. We compare two approaches for setting baselines for flexibility rating using data.
Abstract Quantifying and rating energy flexibility in existing buildings will become increasingly important as building energy services become electrified. Flexibility ratings based on building design specifications have shown potential to complement energy performance certificates and enable the comparison between buildings. However, relying on physical models and standard boundary conditions could lead to a ‘flexibility gap’: a difference between predicted and actual flexibility. This article investigates the incorporation of monitored data into design-based flexibility ratings, using an existing rating methodology and two UK case study domestic buildings. We firstly examine whether the current rating methodology can accept monitored data, and find it is able to apart from the final step of rating. We then devise two methods of calculating the metrics required for the flexibility rating, based not on physical models but on data. Using these methods, we examine the impact of the standard operational modelling assumptions on the flexibility metrics compared to using data-informed inputs, which highlights some discrepancies and some concepts in the flexibility rating methodology for which monitored data may be very difficult to obtain (e.g. recovery time). Finally, we suggest how to improve the usefulness of flexibility ratings by incorporating additional information based on monitored data.
Closing the energy flexibility gap: Enriching flexibility performance rating of buildings with monitored data
https://orcid.org/0000-0002-3735-6006
https://orcid.org/0000-0002-8771-3489
https://orcid.org/0000-0003-0357-2090
https://orcid.org/0000-0003-3466-7850
https://orcid.org/0000-0001-6352-2226
Highlights Energy Flexibility Gap – difference between expected and actual energy flexibility. We look at the part of the Energy Flexibility Gap related to operational factors. Building energy flexibility ratings can be made more realistic with monitored data. We compare two approaches for setting baselines for flexibility rating using data.
Abstract Quantifying and rating energy flexibility in existing buildings will become increasingly important as building energy services become electrified. Flexibility ratings based on building design specifications have shown potential to complement energy performance certificates and enable the comparison between buildings. However, relying on physical models and standard boundary conditions could lead to a ‘flexibility gap’: a difference between predicted and actual flexibility. This article investigates the incorporation of monitored data into design-based flexibility ratings, using an existing rating methodology and two UK case study domestic buildings. We firstly examine whether the current rating methodology can accept monitored data, and find it is able to apart from the final step of rating. We then devise two methods of calculating the metrics required for the flexibility rating, based not on physical models but on data. Using these methods, we examine the impact of the standard operational modelling assumptions on the flexibility metrics compared to using data-informed inputs, which highlights some discrepancies and some concepts in the flexibility rating methodology for which monitored data may be very difficult to obtain (e.g. recovery time). Finally, we suggest how to improve the usefulness of flexibility ratings by incorporating additional information based on monitored data.
Closing the energy flexibility gap: Enriching flexibility performance rating of buildings with monitored data
https://orcid.org/0000-0002-3735-6006
https://orcid.org/0000-0002-8771-3489
https://orcid.org/0000-0003-0357-2090
https://orcid.org/0000-0003-3466-7850
https://orcid.org/0000-0001-6352-2226
Highlights Energy Flexibility Gap – difference between expected and actual energy flexibility. We look at the part of the Energy Flexibility Gap related to operational factors. Building energy flexibility ratings can be made more realistic with monitored data. We compare two approaches for setting baselines for flexibility rating using data.
Abstract Quantifying and rating energy flexibility in existing buildings will become increasingly important as building energy services become electrified. Flexibility ratings based on building design specifications have shown potential to complement energy performance certificates and enable the comparison between buildings. However, relying on physical models and standard boundary conditions could lead to a ‘flexibility gap’: a difference between predicted and actual flexibility. This article investigates the incorporation of monitored data into design-based flexibility ratings, using an existing rating methodology and two UK case study domestic buildings. We firstly examine whether the current rating methodology can accept monitored data, and find it is able to apart from the final step of rating. We then devise two methods of calculating the metrics required for the flexibility rating, based not on physical models but on data. Using these methods, we examine the impact of the standard operational modelling assumptions on the flexibility metrics compared to using data-informed inputs, which highlights some discrepancies and some concepts in the flexibility rating methodology for which monitored data may be very difficult to obtain (e.g. recovery time). Finally, we suggest how to improve the usefulness of flexibility ratings by incorporating additional information based on monitored data.
Closing the energy flexibility gap: Enriching flexibility performance rating of buildings with monitored data
de-Borja-Torrejon, Manuel (author) / Mor, Gerard (author) / Cipriano, Jordi (author) / Leon-Rodriguez, Angel-Luis (author) / Auer, Thomas (author) / Crawley, Jenny (author)
Energy and Buildings ; 311
2024-04-02
Article (Journal)
Electronic Resource
English
Energy Flexible Buildings , Energy Flexibility , Flexibility Gap , Performance rating , Demand response , Demand side management , Flexibility indicators , Building labelling , ARX , Autoregressive Models with Exogenous Variables , CV(RMSE) , Coefficient of Variance of the Root Mean Square Error , EF , EF Gap , Energy Flexibility Gap , EF Gap <inf>MAE</inf> , Mean Absolute Error of the Energy Flexibility Gap , EPC , Energy Performance Certificate , EFSI , Expected Flexibility Saving Index , DHW , Domestic Hot Water , DR , Demand Response , <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>E</mi> <mrow><mi>DR</mi></mrow></msub></math> , Actual Energy Variation [kWh<inf>e</inf>] , HP , Heat Pump , FF , Flexibility Function , FI , Flexibility Index , FPI , Flexibility Performance Indicator [-] , GOF , Good of Fit , HA , House A , HB , House B , HB1 , House B before user adjustment of thermostat , HB2 , House B after user adjustment of thermostat , MAE , Mean Absolute Error , NMBE , Normalised Mean Bias Error , <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mover><mi>P</mi> <mo>̇</mo></mover> <mrow><mi>res</mi></mrow></msub></math> , Committed Power [kW<inf>e</inf>] , PSS , Peak-Shaving Strategy , RMSE , Root Mean Square Error , SRI , Smart Readiness Indicator , TMY , Typical Meteorological Year , <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>t</mi> <mrow><mi>res</mi></mrow></msub></math> , Response Time [h] , <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>t</mi> <mrow><mi>rec</mi></mrow></msub></math> , Recovery Time [h] , WD , Weekday , WE , Weekend Day
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