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1 
The 
Green Energy 
Scenario 
Piergiorgio Cipriano
If You 
Can't 
Measure 
2 
It, 
You Can't 
Improve It
3 
‘‘GGrreeeenn EEnneerrggyy’’ 
mmeeaannss ((bbiigg)) nnuummbbeerrss
4 
Population: 
7.2% of world’s population 
(505 million) 
source: http://epp.eurostat.ec.europa.eu/portal/page/portal/population/data/main_tables 
EEnneerrggyy rreessiiddeennttiiaall ddeemmaanndd:: 
2222%% ooff wwoorrlldd’’ss ddeemmaanndd 
ssoouurrccee:: hhttttpp::////wwwwww..eeiiaa..ggoovv//ffoorreeccaassttss//iieeoo//bbuuiillddiinnggss..ccffmm
5 
Europe 2011 - Energy Flow (MTOE) 
source: http://ec.europa.eu/energy/publications/doc/2013_pocketbook.pdf
6 
In 2020, the European 
consumption of energy will be 
25 trillion kWh 
(25,000,000,000,000) 
In 2040 it will rise to 28 trillion kWh 
source: http://www.eia.gov/forecasts/ieo/
In terms of energy consumption, 
buildings represent around 40% 
7 
source: http://www.europeanclimate.org/documents/LR_%20CbC_study.pdf (and others)
In EU, the gross floor space could be 
concentrated in a land area equivalent to that 
of Belgium (30,528 km2). 
8 
source: http://www.europeanclimate.org/documents/LR_%20CbC_study.pdf
9 
European households are 
responsible for 68% of the 
total final energy use in 
buildings, for: 
-heating (70%) 
-Cooling 
-hot water 
-cooking 
-appliances 
The most used fuel is gas. 
source: http://www.europeanclimate.org/documents/LR_%20CbC_study.pdf
10 
OOuurr ggooaall iiss
OOuurr ggooaall iiss 
… to implement an open data hub based on 
CKAN connected to INSPIRE Download 
Services provided by five pilot cities: 
-Reggio Emilia (IT) 
-Girona (ES) 
-Maroussi (GR) 
-Oeiras (PT) 
-Turku (FI) 
11
12
13
source: http://www.simstadt.eu/en/index.html 14
source: http://en.wiki.modeling.sig3d.org/index.php/2014_S_HfT_EnergyADE 15
SSuummmmaarriissiinngg…… wwee wwiillll ooppeenn 
hhaarrmmoonniisseedd ddaattaa aabboouutt 
eenneerrggyy,, bbuutt wwee aarree ccoonnsscciioouuss 
tthhaatt wwoorrllddwwiiddee tthhee pprroobblleemmss 
ooff eenneerrggyy aarree mmuucchh bbiiggggeerr 
tthhaann ““ooppeenn ddaattaa”” …… 
16
http://www.powerless-film.com/ 17
18 
Thanks 
piergiorgio.cipriano@sinergis.it

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GeoSmartCity - the Green Energy scenario

Editor's Notes

  1. This old man is not my grandfather, but William Thomson (akas Lord Kelvin): he used to say this sentence … Also when talking about “energy” we may say that “if we can’t measure things like energy efficiency at building level, we cannot improve it”
  2. When talking about “Green Energy”, the first thought is often the “energy consumption”: in terms of ICT matters, measures and data about energy consumption mean big data, either in terms of Volume, Velocity and Variety
  3. Total world energy use rises from 524 quadrillion British thermal units (BTU) in 2010 to 630 quadrillion BTU in 2020 and to 820 quadrillion Btu in 2040. In Europe: 85.5 quadrillion BTU (25 trillion) in 2020 and 94.6 in 2040. 1 Btu = 0.00029307106944 kilowatt hours http://www.rapidtables.com/convert/energy/BTU_to_kWh.htm
  4. This percentage is defined in different sources; see: http://www.europeanclimate.org/documents/LR_%20CbC_study.pd http://europa.eu/legislation_summaries/internal_market/single_market_for_goods/construction/en0021_en.htm http://www.iea.org/aboutus/faqs/energyefficiency/ http://www.europeanclimate.org/documents/LR_%20CbC_study.pdf http://ec.europa.eu/energy/observatory/trends_2030/doc/trends_to_2050_update_2013.pdf
  5. The “map” depicted here (background image) represents the “energy need” at building level. For this simple map, you need to integrate at least the following data in order to predict the building energy with sufficient quality: Building footprints (ACI) Building height (or number of floors - ACI) Building use(s) Building age and correspondent building envelope stereotypes Stereotypes for heating/hot-water/ventilation systems The more the geodata of buildings are detailed, the better the prediction. In order to avoid reinventing the wheel, we may consider to “extend” two existing standards: the INSPIRE data specification for “Buildings”, and the OGC CityGML, with an Application Domain Extension for the “Energy Performance of Buildings”. In the model we don’t need interior elements of each building, but we may have a simple LOD3 level to refer elements like the roof, the envelope walls, windows, … use them for large scale analysis, at urban level. We may also use this information to make a comparison with other sources, like aerial thermal images.
  6. The “map” depicted here (background image) represents the “energy need” at building level. For this simple map, you need to integrate at least the following data in order to predict the building energy with sufficient quality: Building footprints (ACI) Building height (or number of floors - ACI) Building use(s) Building age and correspondent building envelope stereotypes Stereotypes for heating/hot-water/ventilation systems The more the geodata of buildings are detailed, the better the prediction. In order to avoid reinventing the wheel, we may consider to “extend” two existing standards: the INSPIRE data specification for “Buildings”, and the OGC CityGML, with an Application Domain Extension for the “Energy Performance of Buildings”. In the model we don’t need interior elements of each building, but we may have a simple LOD3 level to refer elements like the roof, the envelope walls, windows, … use them for large scale analysis, at urban level. We may also use this information to make a comparison with other sources, like aerial thermal images.