Data fusion analysis applied to different climate change models: an application to the energy consumptions of a building office

Risultato della ricerca: Paper

Abstract

The paper aims to achieve the modelling of climate change effects on heating and cooling in the building sector, through the use of the available Intergovernmental Panel on Climate Change forecasted data. Data from several different climate models will be fused with regards to mean air temperature, wind speed and horizontal solar radiation. Several climatic models data were analyzed ranging from January 2006 to December 2100. Rather than considering each model in isolation, we propose a data fusion approach for providing a robust combined model for morphing an existing weather data file. The final aim is simulating future energy use for heating and cooling of a reference building as a consequence of the expected climate changes. We compare results, in terms of robustness to overfitting, for two different fusion methodologies, based on the comparison between errors on punctual historical data or prediction models that can be obtained by each climate simulator and by the actual ERA-INTERIM data set. Finally, we map the new aggregated data into a prediction trace of heating and cooling energy requirements. The expected energy demand is in the range of the one provided by single climate models, with a variability that reaches up to the 10% of the overall energy requirements.
Lingua originaleEnglish
Stato di pubblicazionePublished - 2018

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title = "Data fusion analysis applied to different climate change models: an application to the energy consumptions of a building office",
abstract = "The paper aims to achieve the modelling of climate change effects on heating and cooling in the building sector, through the use of the available Intergovernmental Panel on Climate Change forecasted data. Data from several different climate models will be fused with regards to mean air temperature, wind speed and horizontal solar radiation. Several climatic models data were analyzed ranging from January 2006 to December 2100. Rather than considering each model in isolation, we propose a data fusion approach for providing a robust combined model for morphing an existing weather data file. The final aim is simulating future energy use for heating and cooling of a reference building as a consequence of the expected climate changes. We compare results, in terms of robustness to overfitting, for two different fusion methodologies, based on the comparison between errors on punctual historical data or prediction models that can be obtained by each climate simulator and by the actual ERA-INTERIM data set. Finally, we map the new aggregated data into a prediction trace of heating and cooling energy requirements. The expected energy demand is in the range of the one provided by single climate models, with a variability that reaches up to the 10{\%} of the overall energy requirements.",
author = "Maurizio Cellura and Ilenia Tinnirello and Francesco Guarino and Daniele Croce and Nicola Inzerillo",
year = "2018",
language = "English",

}

TY - CONF

T1 - Data fusion analysis applied to different climate change models: an application to the energy consumptions of a building office

AU - Cellura, Maurizio

AU - Tinnirello, Ilenia

AU - Guarino, Francesco

AU - Croce, Daniele

AU - Inzerillo, Nicola

PY - 2018

Y1 - 2018

N2 - The paper aims to achieve the modelling of climate change effects on heating and cooling in the building sector, through the use of the available Intergovernmental Panel on Climate Change forecasted data. Data from several different climate models will be fused with regards to mean air temperature, wind speed and horizontal solar radiation. Several climatic models data were analyzed ranging from January 2006 to December 2100. Rather than considering each model in isolation, we propose a data fusion approach for providing a robust combined model for morphing an existing weather data file. The final aim is simulating future energy use for heating and cooling of a reference building as a consequence of the expected climate changes. We compare results, in terms of robustness to overfitting, for two different fusion methodologies, based on the comparison between errors on punctual historical data or prediction models that can be obtained by each climate simulator and by the actual ERA-INTERIM data set. Finally, we map the new aggregated data into a prediction trace of heating and cooling energy requirements. The expected energy demand is in the range of the one provided by single climate models, with a variability that reaches up to the 10% of the overall energy requirements.

AB - The paper aims to achieve the modelling of climate change effects on heating and cooling in the building sector, through the use of the available Intergovernmental Panel on Climate Change forecasted data. Data from several different climate models will be fused with regards to mean air temperature, wind speed and horizontal solar radiation. Several climatic models data were analyzed ranging from January 2006 to December 2100. Rather than considering each model in isolation, we propose a data fusion approach for providing a robust combined model for morphing an existing weather data file. The final aim is simulating future energy use for heating and cooling of a reference building as a consequence of the expected climate changes. We compare results, in terms of robustness to overfitting, for two different fusion methodologies, based on the comparison between errors on punctual historical data or prediction models that can be obtained by each climate simulator and by the actual ERA-INTERIM data set. Finally, we map the new aggregated data into a prediction trace of heating and cooling energy requirements. The expected energy demand is in the range of the one provided by single climate models, with a variability that reaches up to the 10% of the overall energy requirements.

UR - http://hdl.handle.net/10447/323810

M3 - Paper

ER -