Background
Heating and cooling constitutes around half of the EU's final energy consumption and is the biggest energy end-use sector, ahead of transport and electricity.
As most of heating and cooling energy (about 85%) is produced from natural gas, coal, oil products and only 15% is generated from renewable energy, heating and cooling sector has a crucial role in planning EU's transition towards an energy efficient and decarbonized energy system. In this pathway knowing the current energy demand and assessing the requirements expected in the coming decades becomes necessary.
Method
Energy demand for heating/cooling depends closely on surface temperature variability (with maximum energy requirements correlated to temperature extreme values). As the relationship between heating/cooling and temperature is not linear, the indices “heating degree-days” and “cooling degree-days” are used , in order to estimate energy demand.
They are defined as the difference between a so called base-temperature (with respect to which the energy demand is minimal) and the outside daily mean temperature. Conventionally the heating and cooling degree-days are computed at annual scale (HDD and CDD respectively), by adding daily differences for every day of the year.
In this study the degree-days have been estimated for Italy in the current and future climate, by using daily temperature values provided by two reference observed data-sets (E-OBS 1961÷2016, MESAN 1979÷2013) and 10 ENSEMBLES climate model simulations (1961÷2050). Two methodologies have been applied: JRC/MARS (J, the European reference method) and Giannakopoulus (G, used in the European Project ENSEMBLES in assessing the climate change impacts on the Mediterranean Basin). They differ essentially in using different base-temperatures. At first the degree-days have been estimated by considering uniformly all the grid points on the national territory. Then, the population spatial distribution has also been considered in computing the degree-days indexes, as proxy data of residential buildings’ locations , as energy demand is correlated to the building volumes.
Principal Findings
As expected, the results gathered from the two methods are substantially different: the G degree-days are considerably lower than the J estimates. Respect to the degree-days calculated by considering the grid-points uniformly, the weighted-population HDD resulted lower (~20%) than the first estimates, whereas the CDD resulted a bit higher or, at least, similar to first ones. About the degree-day variations expected in the next decades, J projections show a noteworthy HDD reduction (~25% in 2050) and a significant CDD increase (~100%) according with the expected warming. By using G method, the HDD reduction is similar to J results, instead it is found a sharper increase of CDD (~140% in the middle of the century).
Conclusions
The degree-days scenarios elaborated could be improved by considering other meteorological variables, like relative humidity, solar radiation, wind speed, as all of them could influence the energy consumption in house conditioning. Actually, the specific characteristics of private sector housing should be taken into account for an appropriate energy evaluation.
Energy consumption scenarios will to be elaborated as soon as energy consumption data will be available at daily scale, with a sufficient spatial resolution.