Building stock in Helsinki: Difference between revisions

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Ratio of wall area / effective area|-|0.00|0.00|0.00|0.00|0.00|PPT
Ratio of wall area / effective area|-|0.00|0.00|0.00|0.00|0.00|PPT
Ratio of window/effective area|-|0.15|0.00|0.00|0.00|0.00|PPT
Ratio of window/effective area|-|0.15|0.00|0.00|0.00|0.00|PPT
U-value of windows|W /m2 /K|1.0|1.4|1.4|0.00|0.00|PPT
U-value of windows|W /m2 /K|1.0|1.4|1.4|1.4|1.6|PPT
Solar heat gain coefficient "G-Value" of windows|%|0,40|0.0|0.0|0.0|0.0|PPT
Solar heat gain coefficient "G-Value" of windows|%|0,40|0.0|0.0|0.0|0.0|PPT
Efficiency increase of G-value of windows|% /a|0.0|0.0|0.0|0.0|0.0|Excel
Efficiency increase of G-value of windows|% /a|0.0|0.0|0.0|0.0|0.0|Excel
Efficiency increase of U-value of windows|% /a|0.0|0.0|0.0|0.0|0.0|Excel
Efficiency increase of U-value of windows|% /a|0.0|0.0|0.0|0.0|0.0|Excel
U-value of building walls|W /m2 /K|0.16|0.28|0.25|0.00|0.00|PPT
U-value of building walls|W /m2 /K|0.16|0.25|0.25|0.28|0.3|PPT
</t2b>
</t2b>


Line 174: Line 174:
Economidou, M., Atanasiu, B., Despret, C., Maio, J., Nolte, I., & Rapf, O. (2011). Europe’s buildings under the microscope. A Country-by-country review of the energy performance of buildings, 131.
Economidou, M., Atanasiu, B., Despret, C., Maio, J., Nolte, I., & Rapf, O. (2011). Europe’s buildings under the microscope. A Country-by-country review of the energy performance of buildings, 131.


Unit heat consumption, opasnet
Kragh, J., Laustsen, J. B., & Svendsen, S. (2008). Proposal for Energy Rating System of windows in EU. DTU Civil Engineering-Report R-201.
Kragh, J., Laustsen, J. B., & Svendsen, S. (2008). Proposal for Energy Rating System of windows in EU. DTU Civil Engineering-Report R-201.



Revision as of 16:51, 11 May 2015



Question

What is the building stock in Helsinki?

Important reminder: Always include the references to the tables: where did you get the value and how. A value without the reference is useless because it's not possible for others to track how it was derived.

If you couldn't find a value, write down where you tried to search. This is important because then nobody else needs to search the same sources again for nothing. Thank you!

Answer

Rationale

Data

----#: . CHECK ALSO THIS DATA: --Jouni (talk) 12:53, 5 May 2015 (UTC) (type: truth; paradigms: science: comment)

Building stock

The structures of the tables are based on CyPT Excel file N:\YMAL\Projects\ilmastotiekartta\Helsinki Data Input Template - Building Data.xlsx.

Table 1. Effective floor area of buildings by building type.

Type of data:

  • Average, City
  • Example, city
  • Average, city close by
  • Example, city close by
  • Average, national
  • Example, country

Quality of data

  • Statistics
  • Extrapolated
  • Calculated from statistics
  • Calculated from stat. inc. assumptions

You have error(s) in your data:

Number of indices and result cells does not match

Effective floor area of buildings(m2)
ObsBuildingBaseline202020252050Year of baselineType of dataQualityDescription
1Residential28304514315878623487121039796232Building block of Helsinki area, April 2015
2Public85073118586286865693290018087Building block of Helsinki area, April 2015
3Industrial3182152324123232945683569457Building block of Helsinki area, April 2015
4Other10699447107987711088762211341838Building block of Helsinki area, April 2015

Ref. Helsinki master plan for 2050: there are 860 000 citizens living in Helsinki (ref. www.yleiskaava.fi, visio2050); Residental buildings => fast growth Prediction of citizen number in Helsinki in 2020, 2030, 2040 and 2050 was used for calculations (ref. Helsingin 30% päästövähennysselvitys). Helsinki’s climate policy: 30% reduction in emissions: In 2010 the proportion of jobs in services and public sectors was 94%, and in industry 6%. In 2020 the proportion of jobs in services and public sectors is estimated to be 96%, and in industry 4%. Public and other buildings => between fast growth option and basic option, Industry=> Basic option Prediction of job number in Helsinki in 2020, 2030, 2040 and 2050 was used for calculations (ref. Helsingin 30% päästövähennysselvitys).

Notes
Sheet 4_Input Buildings (Area Demand). Priority 1. Auxiliaries PPT. Absolute increase/decrease rate will be based on the inhabitants projected in time.
This is another list that was considered but rejected as too complex: Residential buildings, Government & public administration buildings, Commercial offices buildings, Data centers buildings, Education and K12 and universitiy buildings, Hospitals and healthcare buildings, Hotels and hospitality and leisure buildings, Exhibitions and fairs and halls buildings, Retail and stores and shops buildings, Warehouses & shopping mall buildings, Industrial buildings, Non residential buildings unspecified

Table 2. Existing situation of important energy parametres in the building stock.

Existing situation of total stock(%)
ObsPropertyResidentialPublicIndustrialOtherDescription
1Wall insulation17Default building data. excel/cyPT data collection
2High efficient glazing35Energy performance class A in building automation/Simens pdf document searched in internet
3Efficient lighting in baseline1,4Default building data. excel
4Demand oriented lighting26,9Default building data. excel
5Building Efficiency Monitoring
6Building Remote Monitoring
7Building Performance Optimization
8Demand controlled ventilation16,2or 22,4Default building data. excel/cyPT data collection
9Heat and Cold Recovery in ventilation17,8
10Efficient Motors
11Building Automation BACS Class C
12Building Automation BACS Class B
13Building Automation BACS Class A
14Room Automation HVAC30http://www.buildingtechnologies.siemens.com
15Room Automation HVAC + lighting
16Building Automation HVAC + lighting + blinds60www.buildingtechnologies.siemens.com
Notes
Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 1. Auxiliaries PPT.

Energy demand

Table 3. Total energy demand by energy type and building type.

Total energy demand by type(kWh /m2 /a)
ObsEnergy typeResidentialPublicIndustrialOtherDescription
1Electricity
2Cooling
3Heating
Notes
Heating includes warm water. Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 1. Auxiliaries PPT.

Table 4. Shares of different energy sinks by building type.

Share of energy demand by use type(kWh/m2a)
ObsEnergy typeUseResidentialOtherAdminIndustryDescriptionAuxiliaries
1CoolingInfiltration0.00.00.00.0PPT
2CoolingVentilation0.00.00.09.0PPT
3CoolingLosses through walls through transmission0.00.00.00.0PPT
4CoolingHeat input by solar radiation through windows0.00.00.00.0PPT
5CoolingLosses through windows through transmission0.00.00.00.0PPT
6CoolingOther effects (e.g. people, electrical Appliances)0.00.00.00.0PPT
7HeatingInfiltration0.00.00.00.0PPT
8HeatingVentilation29.1927.5627.2227.22PPT
9HeatingWalls34.7532.8132.4032.40PPT
10HeatingWindows15.4914.6314.4414.44PPT
11HeatingFloors8.748.258.158.15PPT
12HeatingRoofs9.338.818.708.70PPT
13HeatingOther0.00.00.00.0PPT
14HeatingWarm water1.890.01.761.76Excel
15ElectricityLighting2.8220.0549.688.67Excel
16ElectricityAppliances24.8418.7414.1913.00Excel
17ElectricityVentilation012.0321.2918.24Excel
18ElectricityOther0.5623.6956.7751.76Excel

⇤--#: . The problem with your table is that you have changed the columns. You cannot do that, because the original classification is what we use in the model. Instead, you have to extrapolate from the existing numbers to those classes needed in the model. --Jouni (talk) 06:45, 29 April 2015 (UTC) (type: truth; paradigms: science: attack)

----#: . Use decimal points instead of decimal commas. --Jouni (talk) 06:45, 29 April 2015 (UTC) (type: truth; paradigms: science: comment)

----#: . Also include references and links so that the reader can go back to the original data and see where the numbers came from. --Jouni (talk) 06:45, 29 April 2015 (UTC) (type: truth; paradigms: science: comment)

Notes
Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 1. Auxiliaries: see table.

Data from Climate policies Helsinki additional data

We went through the data mentioned above and also the files given in "Climate policies Helsinki data" Climate policies Helsinki data but could not find the data for cooling, heating:infiltration, heating:other, heating:warm water:other and electricity:ventilation:resindential.

Total estimated amount of energy needed for cooling 23504480 kWh/a (whole Helsinki). Based on http://www.energiatehokashelsinki.fi/energiankulutus/Helsinki 14332 GWh/a for housing (total energy demand minus traffic 18 % [1] ) and http://www.korjaustieto.fi/pientalot/pientalojen-energiatehokkuus/energiatehokkuus-pientaloissa/pientalon-energiankulutus-ja-paastot.html 2 % of total energy demand of housing used for cooling. Total floor area of Helsinki needed (table 1).

Table 5. Changes in energy efficiency of different energy sinks.

Efficiency increase decreasing energy demand(% /a)
ObsEnergy typeUseResidentialPublicIndustrialOtherDescription
1ElectricityLighting /Lamp stock0.60.60.60.6
2ElectricityAppliances0.60.60.60.6
3ElectricityVentilation0.60.60.60.6
4ElectricityOther0.60.60.60.6
5CoolingInfiltration20.60.60.6
6CoolingVentilation20.60.60.6
7CoolingOther (e.g. electric appliances, people)20.60.60.6
8HeatingInfiltration0.60.60.60.6
9HeatingVentilation0.60.60.60.6
10HeatingFloors0.60.60.60.6
11HeatingRoofs0.60.60.60.6
12HeatingOther reasons0.60.60.60.6
13HeatingWarm water1.10.60.60.6
Notes
Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 3. Auxiliaries Excel.

Estimated 3 % of houses renovated/year and 20 % increase in energy efficiency when renovated. [2] Data apart from 0.6% values from Climate policies Helsinki additional data

The presentation about tables 4 and 5 PresentationHW9

Table 6. Important energy parameters.

Energy parameters(-)
ObsParameterUnitResidentialPublicIndustrialOtherDescriptionAuxiliary
1Ratio of wall area / effective area-0.000.000.000.000.00PPT
2Ratio of window/effective area-0.150.000.000.000.00PPT
3U-value of windowsW /m2 /K1.01.41.41.41.6PPT
4Solar heat gain coefficient "G-Value" of windows%0,400.00.00.00.0PPT
5Efficiency increase of G-value of windows% /a0.00.00.00.00.0Excel
6Efficiency increase of U-value of windows% /a0.00.00.00.00.0Excel
7U-value of building wallsW /m2 /K0.160.250.250.280.3PPT
Notes
Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 3. Auxiliaries: see table.

Economidou, M., Atanasiu, B., Despret, C., Maio, J., Nolte, I., & Rapf, O. (2011). Europe’s buildings under the microscope. A Country-by-country review of the energy performance of buildings, 131.

Unit heat consumption, opasnet Kragh, J., Laustsen, J. B., & Svendsen, S. (2008). Proposal for Energy Rating System of windows in EU. DTU Civil Engineering-Report R-201.

http://www.ziegel.at/gbc-ziegelhandbuch/eng/ressourcen/energie/graue.htm

Lighting

Tables 7-8. Run times and shares of lamps by lamp type and building type.

Average run time of lamps per year(h /a)
ObsLampResidentialPublicIndustrialOtherDescription
1Incandescent lamps
2Compact fluorescent lamps
3Fluorescent lamps
4Low voltage halogen lamps
5Medium voltage halogen lamps
Shares of lamp types(%)
ObsLampResidentialPublicIndustrialOtherDescription
1Incandescent lamps
2Compact fluorescent lamps
3Fluorescent lamps
4Low voltage halogen lamps
5Medium voltage halogen lamps
Notes
Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 3. Auxiliaries Excel.

Other

Sheet 4_Input Buildings (Area Demand). Priority 1. Auxiliaries PPT. Nice to have specific factor otherwise we adapt standard data or other studies.

Possibly important information
Data Result
Number of households
Number of persons per household

Calculations

See also

References

Related files