Energy demand indicator dataset in Climate Guide

Climate change causes effects on the energy demand for cooling and heating of buildings with warmer temperature increasing the demand for cooling in the summer and decreasing the demand for heating. Degree-day indicators, calculated as the accumulated sum of temperature above (for cooling) or below (for heating) a threshold temperature, serve as a proxy for the energy demand. This dataset consists of cooling and heating energy demand indicators calculated on a regular 10 km x 10 km grid over Finland under current and future climate.

Data processing

30-year average are shown on a regular 10 km x 10 km over Finland for changes between the baseline period 1961-1990 and three future periods, 2010–2039, 2040–2069 and 2070–2099, under three future scenarios that were selected from the CMIP3 ensemble: low emissions (SRES B1) simulated with the HadCM3 Global Climate Model (GCM), medium emissions (SRES B2) expressed as the mean of six GCMs (see Fronzek et al. 2007), and high emissions (SRES A1FI)/ECHAM4 GCM.

A relative measure of demand for air conditioning is the sum of temperatures exceeding a threshold temperature above which cooling would be required to maintain a comfortable room temperature (cooling degree-days). In Finland, the energy consumption of a building for cooling is almost proportional to the indoor and outdoor temperature difference. Here average CDD are calculated by accumulating mean daily temperatures throughout the year that lie above a threshold of 18°C (Fronzek et al. 2007).

The demand for space heating is estimated as the sum of temperatures lower than a threshold temperature below which heating would be required to maintain a comfortable room temperature (heating degree-days or HDD). In Finland, the energy consumption of a building for heating is almost proportional to the indoor and outdoor temperature difference. Here average HDD are calculated by accumulating mean daily temperatures throughout the year that lie below a threshold of 18°C (30-year period). The analysis was carried out for 30-year averages, results are presented as the change between a future period and the baseline period 1961-1990.

The following indicators are included:
Cooling demand (unit: °Cd or degree-days above 18°C)
Heating demand (°Cd or degree-days below 18°C)

Data access

https://paikkatiedot.ymparisto.fi/geoserver/ilmo-climateguide/wms?request=getcapabilities

The data can be accessed via a Web Feature Service (WFS) supporting versions 1.0.0 and 1.1.0. WFS services can be opened in various GIS applications, for example ArcGIS for Desktop and open source desktop application QGIS. Typically, the GIS applications loads data from a WFS service in a GML (Geography Markup Language) format. Additionally, the data can be loaded as zip compressed ESRI Shape files, a CSV file or a JSON file. The service includes 2 feature types representing energy demand simulations:
energy_coolingdegreedays – change in cooling degree-days above 18 °C (%), energy_heatingdegreedays – change in heating degree-days below 18 °C (%).

The attributes of each feature type are labelled as [climateModel]_[emissionScenario]_[timePeriod]_0_[valueType]. There are three climate models, hadcm3, mean (6-GCM ensemble mean) and eh4opyc; and three emission scenarios b1, b2 and a1fi. The timePeriod includes the start year and the end year of the period. There is one option for valueType, “rc” for relative change. For example, the attribute labeled as hadcm3_b1_20702099_0_rc describes the climate model HadCM3, the emission scenario SRES B1, the time period from 2070 to 2099 and relative change. In addition, there is an attribute labelled control_na_19611990_0_av with the absolute values (valueType “av”) for the baseline period 1961-1990 (in degree-days).