Building stock in Helsinki

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Question

What is the building stock in Helsinki?

Rationale

Data

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

Effective floor area of buildings(m2)

Obs	Building	Baseline	2020	2025	2050	Year of baseline	Type of data	Quality	Description
1	Residential
2	Public
3	Industrial
4	Other

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(%)

Obs	Property	Residential	Public	Industrial	Other	Description
1	Wall insulation
2	High efficient glazing
3	Efficient lighting in baseline
4	Demand oriented lighting
5	Building Efficiency Monitoring
6	Building Remote Monitoring
7	Building Performance Optimization
8	Demand controlled ventilation
9	Heat and Cold Recovery in ventilation
10	Efficient Motors
11	Building Automation BACS Class C
12	Building Automation BACS Class B
13	Building Automation BACS Class A
14	Room Automation HVAC
15	Room Automation HVAC + lighting
16	Building Automation HVAC + lighting + blinds

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)

Obs	Energy type	Residential	Public	Industrial	Other	Description
1	Electricity
2	Cooling
3	Heating

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)

Obs	Energy type	Use	Residential	Other	Admin	Industry	Description	Auxiliaries
1	Cooling	Infiltration	0.0	0.0	0.0	0.0		PPT
2	Cooling	Ventilation	0.0	0.0	0.0	9.0		PPT
3	Cooling	Losses through walls through transmission	0.0	0.0	0.0	0.0		PPT
4	Cooling	Heat input by solar radiation through windows	0.0	0.0	0.0	0.0		PPT
5	Cooling	Losses through windows through transmission	0.0	0.0	0.0	0.0		PPT
6	Cooling	Other effects (e.g. people, electrical Appliances)	0.0	0.0	0.0	0.0		PPT
7	Heating	Infiltration	0.0	0.0	0.0	0.0		PPT
8	Heating	Ventilation	29.19	27.56	27.22	27.22		PPT
9	Heating	Walls	34.75	32.81	32.40	32.40		PPT
10	Heating	Windows	15.49	14.63	14.44	14.44		PPT
11	Heating	Floors	8.74	8.25	8.15	8.15		PPT
12	Heating	Roofs	9.33	8.81	8.70	8.70		PPT
13	Heating	Other	0.0	0.0	0.0	0.0		PPT
14	Heating	Warm water	1.89	0.0	1.76	1.76		Excel
15	Electricity	Lighting	2.82	20.05	49.68	8.67		Excel
16	Electricity	Appliances	24.84	18.74	14.19	13.00		Excel
17	Electricity	Ventilation	0	12.03	21.29	18.24		Excel
18	Electricity	Other	0.56	23.69	56.77	51.76		Excel

⇤--#: . 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

Total estimated amount of energy needed for cooling 23504480 kWh (whole Helsinki). Based on http://www.energiatehokashelsinki.fi/energiankulutus/Helsinki 14332 GWh for housing (total energy demand minus traffic) 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)

Obs	Energy type	Use	Residential	Public	Industrial	Other	Description
1	Electricity	Lighting /Lamp stock	0.6	0.6	0.6	0.6
2	Electricity	Appliances	0.6	0.6	0.6	0.6
3	Electricity	Ventilation	0.6	0.6	0.6	0.6
4	Electricity	Other	0.6	0.6	0.6	0.6
5	Cooling	Infiltration	2	0.6	0.6	0.6
6	Cooling	Ventilation	2	0.6	0.6	0.6
7	Cooling	Other (e.g. electric appliances, people)	2	0.6	0.6	0.6
8	Heating	Infiltration	0.6	0.6	0.6	0.6
9	Heating	Ventilation	0.6	0.6	0.6	0.6
10	Heating	Floors	0.6	0.6	0.6	0.6
11	Heating	Roofs	0.6	0.6	0.6	0.6
12	Heating	Other reasons	0.6	0.6	0.6	0.6
13	Heating	Warm water	1,1	0.6	0.6	0.6

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. Data apart from 0.6% values from http://www.hel.fi/static/rakvv/ohjeet/energiatehokkuus_korjaamisessa.pdf

Table 6. Important energy parameters.

Energy parameters(-)

Obs	Parameter	Unit	Residential	Public	Industrial	Other	Description	Auxiliary
1	Ratio of wall area / effective area	-	0.00	0.00	0.00	0.00	0.00	PPT
2	Ratio of window/effective area	-	0.00	0.00	0.00	0.00	0.00	PPT
3	U-value of windows	W /m2 /K	0.00	0.00	0.00	0.00	0.00	PPT
4	Solar heat gain coefficient "G-Value" of windows	%	0.0	0.0	0.0	0.0	0.0	PPT
5	Efficiency increase of G-value of windows	% /a	0.0	0.0	0.0	0.0	0.0	Excel
6	Efficiency increase of U-value of windows	% /a	0.0	0.0	0.0	0.0	0.0	Excel
7	U-value of building walls	W /m2 /K	0.00	0.00	0.00	0.00	0.00	PPT

Notes

Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 3. Auxiliaries: see table.

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)

Obs	Lamp	Residential	Public	Industrial	Other	Description
1	Incandescent lamps
2	Compact fluorescent lamps
3	Fluorescent lamps
4	Low voltage halogen lamps
5	Medium voltage halogen lamps

Shares of lamp types(%)

Obs	Lamp	Residential	Public	Industrial	Other	Description
1	Incandescent lamps
2	Compact fluorescent lamps
3	Fluorescent lamps
4	Low voltage halogen lamps
5	Medium 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

See also

• Descriptions about the summary calculations on sheet Parameter Balance (not needed any more).
• Rakennustietoruudukko pääkaupunkiseudulta [1]