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Poster RSE 16070893

Estimating degree days

Poster

Annual Conference of the Italian Society for Climate Sciences (SISC) Cagliari 19-20, Ottobre-2016.

Request Document (2.05 MB, .pdf)

P. Faggian (RSE SpA), G. Riva (RSE SpA)

SCENARI 2016 - Analysis and electrical scenarios, energy, environmental

The energy request depends closely on surface temperature variability (with maximum energy requirements correlated to temperature extreme values). The effect of temperature impacts on heating/cooling energy use have been analyzed by considering heating (HDD) and cooling (CDD) degree-days.Two methodologies to compute such indexes (JRC/MARS-EUROSTAT and Giannakopoulus) have been considered to assess the sensitivity of the two procedures applied over Italy.The daily temperature input have been provided by the gridded data E-OBS and a sub-set of seven ENSEMBLES models at 25 km spatial resolution. The first data-set allowed to investigate the annual degree-days from 1961 to 2014 against the national data provided by JRC/EUROSTAT, as well as their spatial distribution; the model simulations let to estimate the changes expected in the next decades.Moreover, as the energy demand is strongly correlated to the building dimensions to be heated/cooled, the population distribution has been considered as proxy data to improve HDD and CDD national average estimates. As the urban areas are not distributed homogeneously over Italy, significant differences has been found in the new values.On the basis of models results and in accordance with previous studies climate change will result in reduction in demand for heating and increases everywhere in demand for cooling.

An important aspect dealing with the impacts of climate change on the urban environment may be assessed by considering the modification in the energy demand for heating and cooling of buildings. As the energy request depends closely on surface temperature variability (with maximum energy requirements correlated to temperature extreme values), the effect of temperature impacts on heating/cooling energy use have been analyzed by considering heating (HDD) and cooling (CDD) degree-days.

Two methodologies to compute such indexes (JRC/MARS-EUROSTAT [1,2] and Giannakopoulus [3]), which substantially differentiate one from each other for the reference temperature considered for quantify the deviation (in degree Celsius) of the daily temperature from it, have been considered to assess the sensitivity of the two procedures applied over Italy.

The daily temperature input have been provided by the gridded data E-OBS and a sub-set of seven ENSEMBLES models at 25 km spatial resolution. The first data-set allowed to investigate the annual degree-days from 1961 to 2014 against the national data provided by JRC/EUROSTAT, as well as their spatial distribution; the model simulations let to estimate the changes expected in the next decades, after a bias correction has been applied and a validation of their performances has been checked.

Moreover, as the energy demand is strongly correlated to the building dimensions to be heated/ cooled, the population distribution has been considered as proxy data to improve HDD and CDD national average estimates. As the urban areas are not distributed homogeneously over Italy, significant differences has been found in the new values: HDD decrease substantially and CDD increase lightly as most of population live in low altitudes area.

On the basis of models results and in accordance with previous studies climate change will result in reduction in demand for heating and increases everywhere in demand for cooling.

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