SCCER Future Energy Efficient Buildings & Districts (FEEB&D)

Data mining for Urban Decentralized Energy Systems

WP3: Urban Decentralized Energy Systems

Geo dependent heat demand model

Combining information from the GWR (OFS 2014) with indicators computed from calibration sets, allow estimating for each building: the heated surface AE (m2), the specific heat demand E (MJ/m2 year) and the final energy demand EB (MJ/year).

Figure 1. Overview of the heat demand estimation model

For each pixel of territory, the estimated heat demand is summed over all buildings, and a bootstrap algorithm estimates a confidence interval.

Results and samples of maps

Figure 2 displays a heat demand map with a zoom on the region of Basel. The total final estimated demand for Switzerland is 94 (TWh), that is close to national energy demand statistics (Kemmler et al. 2014).

Figure 2. Heat demand map of Swiss building stock (200 by 200 (m) raster)

For further reading please refer to: (Schneider and Hollmuller 2015), (Schneider, Khoury, et al. 2016), (Schneider et al. 2017) and (Schneider 2016).

Geo dependent electricity demand model

In collaboration with industrial partner Services Industriels de Genève we update the model developed by (Le Strat 2011) to estimate for each Swiss municipality the electric load curve decomposed into main electric appliances. The ultimate goal of the resulting ElectroWhat platform is to answer the questions:

  • What activity consumes how much, where, when and for what usage.
  • Evaluate the saving potential of efficiency programs.
  • To automatically generate the description of a global program composed of the priority action plans for the territory under study.

Electricity demand maps and estimated load curves

The map of Figure 3 displays the estimated yearly demand per capita with a colour ramp and the fraction of total consumed electricity by four main activity sectors in a pie chart.

Figure 3. Electricity demand map of Swiss building stock

For a selected municipality the tool generates estimated load curves by groups of electric appliances.  Figure 4 shows such an example for the town of Zürich (BSF no. 261).

Figure 4. Load curve by appliance for a working day

GIS energy demand/supply web service

The GIS data is also shared by the way of a web-service (Schneider, Assouline, et al. 2016). More information on https://wisesccer1.unige.ch/.

For more information, contact Dr. Stefan Schneider, UNIGE.

 

References

Kemmler, Andreas, Alexander Piégsa, Andrea Ley, Philipp Wüthrich, Mario Keller, Martin Jakob, and Giacomo Catenazzi. 2014. “Analyse Des Schweizerischen Energieverbrauchs 2000 – 2013 Nach Verwendungszwecken”. Bundesamt für Energie Bern.

OFS. 2014. “Eidgenössisches Gebäude- Und Wohnungsregister,Web Services,Technisches Dossier Zum Datenaustausch  via WebServices”. Bundesamt für Statistik BFS.

Schneider, Stefan. 2016. Swiss Buildings GIS Heat Demand Database, User Manual. D37 Annex. SCCER FEEB&D Delivery Reports. UNIGE.

Schneider, Stefan, Dan Assouline, Nahid Mohajeri, and Jean-Louis Scartezzini. 2016. Integration of Data Energy Demand and Renewable Sources DataD37. SCCER FEEB&D Delivery Reports. UNIGE and EPFL-LESO. https://wisesccer1.unige.ch/.

Schneider, Stefan, and Pierre Hollmuller. 2015. Geo-dependent Energy Demand. Bottom-up Model to Estimate Yearly Heat Demand. SCCER FEEB&D Delivery Reports. UNIGE.

Schneider, Stefan, Jad Khoury, Bernard Lachal, and Pierre Hollmuller. 2016. “Geo-dependent Heat Demand Model of the Swiss Building Stock.” Sustainable Built Environment Regional Conference. SBE 2016, Zurich, June 15-17, SBE Series 2016-17 edition. https://archive-ouverte.unige.ch/unige:84240.

Schneider, Stefan, Jad Khoury, Bernard Marie Lachal, and Pierre Hollmuller. 2017. “Geo-dependent Heat Demand Model of the Swiss Building Stock.” Building Research & Information (Submitted).

Le Strat, Pascale. 2011. Programme EOSh De Maîtrise De La Demande d’Electricité517 421 202 00014. 44500 La Baule, France: APOGEE.