Difference between revisions of "Building stock in Helsinki"

Revision as of 08:20, 12 May 2015

Question

What is the building stock in Helsinki?

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Answer

Rationale

Data

--# : CHECK ALSO THIS DATA: --Jouni (talk) 12:53, 5 May 2015 (UTC)

• Unit heat consumption of buildings in Finland
• http://era17.fi/wp-content/uploads/2010/10/sitran_selvityksia_39.pdf page 25
• http://www.teeparannus.fi/attachements/2010-12-21T11-54-1114846.pdf pages 8-9, also 40-45
• http://www.vtt.fi/inf/pdf/tiedotteet/2007/T2377.pdf page 20-21, also 30, 80
• Heating consumption of buildings

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

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.

Notes

Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 1. Auxiliaries PPT. The numbers founded are not reliable but most of the high technology buildings such as isulated walls, windows.... are started to build from 10 to 15 years ago, so if we could find the building area in the year 2000, we could sustract that from the building area in Helsinki at the moment and get the building area which is built during these 10-15 years and they are high tec building areas with regard to 2 % renovation rate and rebuilding which is on going every year which sould be added to the total amount.

Energy demand

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

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.

⇤# : 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)

--# : Use decimal points instead of decimal commas. --Jouni (talk) 06:45, 29 April 2015 (UTC)

--# : 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)

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.

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.

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.

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.

These tables are based on FACTA database classifications and their interpretation for assessments.

Calculations

See also

• Descriptions about the summary calculations on sheet Parameter Balance (not needed any more).
• Rakennustietoruudukko pääkaupunkiseudulta [3]

References

Related files