Abstract
In 2015, space heating and domestic hot water production accounted for around 40% of the Swiss final energy consumption. Reaching the goals of the 2050 energy strategy will require significantly reducing this share despite the growing building stock. Renewables are numerous but subject to spatial-temporal constraints. Territorial planning of energy distribution systems enabling the integration of renewables requires having a spatial-temporal characterization of the energy demand. This paper presents two bottom-up statistical extrapolation models for the estimation of the geo-dependent heat and electricity demand of the Swiss building stock. The heat demand is estimated by means of a statistical bottom-up model applied at the building level. At the municipality level, the electricity load curve is estimated by combining socio-economic indicators with average consumption per activity and/or electric device. This approach also allows to break down the estimated electricity demand according to activity type (e.g. households, various industry & service activities) and appliance type (e.g. lighting, motor force, fridges). The total estimated aggregated demand is 94 TWh for heat and 58 TWh for electricity, which represent a deviation of 2.9% and 0.5% respectively compared to the national energy consumption statistics. In addition, comparisons between estimated and measured electric load curves are done to validate the proposed approach. Finally these models are used to build a geo-referred database of heat and electricity demand for the entire Swiss territory. As an application of the heat demand model, a realistic saving potential is estimated for the existing building stock; this potential could be achieved through by a deep retrofit program. One advantage of the statistical bottom-up model approach is that it allows to simulate a building stock that replicates the diversity of building demand. This point is important in order to correctly account for the mismatch between gross and net energy saving potential, often called performance gap. The impact of this performance gap is substantial since the estimated net saving potential is only half of the gross one.
Highlights
The global energy statistic report (BFE, 2016) and the sectorial energy consumption analysis of Kemmler et al (2014) show that around 40% of the Swiss final energy consumption is used for space heating (SH) and domestic hot water (DHW) production
Adding the energy consumed by other building services to heat demand, shows that around half of the total final energy consumption can be attributed to the building sector
It is of interest to derive some statistics aggregated at the national level and to compare them with other available statistics
Summary
The global energy statistic report (BFE, 2016) and the sectorial energy consumption analysis of Kemmler et al (2014) show that around 40% of the Swiss final energy consumption is used for space heating (SH) and domestic hot water (DHW) production. Adding the energy consumed by other building services to heat demand, shows that around half of the total final energy consumption can be attributed to the building sector. For this reason, the 2050 Swiss Energy Strategy targets to reduce the demand of the Swiss building stock by 63%, while increasing the share of renewable energy (Kirchner et al, 2012). Using Kaya’s identity, Mavromatidis et al (2016) emphasize that reaching these targets will require to work along two axes: energy efficiency to reduce energy demand and substitution of fossil resources by renewables to reduce emissions per produced energy unit. The project is based on a spatial–temporal characterization of energy demand and renewable energy supply; one of its tasks is to design optimal decentralized energy systems that are able to match both the demand and supply side
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