Helsinki energy consumption: Difference between revisions

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<math>U = \frac{6921.65 GWh/a /(24 h / d \times 365 d/a)}{38990000 m^2 (17 K - 4.8 K)} = 1.661 \frac{W}{m^2 K}</math>
<math>U = \frac{6921.65 GWh/a /(24 h / d \times 365 d/a)}{38990000 m^2 (17 K - 4.8 K)} = 1.661 \frac{W}{m^2 K}</math>


The annual average ambient temperature is 2.5 °C in Kuopio ([[Ambient temperature in Urgenche cities]]). Therefore, we could use hourly temperature data from Kuopio if we add the temperature difference 2.3 K.
The annual average ambient temperature is 2.5 °C in Kuopio ([[Ambient temperature in Urgenche cities]]). Therefore, we could use hourly temperature data from Kuopio if we add the temperature difference 2.3 K. [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=7ZTbz88D8jv2O5Uj Graphs of the data].


<rcode name="temperatures" label="Initiate temperatures" embed=1 graphics=1>
<rcode name="temperatures" label="Initiate temperatures" embed=1 store=1
variables="name:server|type:hidden|default:TRUE">
## This is code is Op_en7317/temperatures [[Helsinki energy consumption]]
## This is code is Op_en7317/temperatures [[Helsinki energy consumption]]
library(OpasnetUtils)
library(OpasnetUtils)
library(ggplot2)


ta <- opbase.data("Op_en6315", subset = "2014-5/2015")
ta <- opbase.data("Op_en6315", subset = "2014-5/2015")
dates <- data.frame(
hours <- as.character(ta$Date)
EN = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"),  
 
FI = c("Tammi", "Helmi", "Maalis", "Huhti", "Touko", "Kesä", "Heinä", "Elo", "Syys", "Loka", "Marras", "Joulu")
if(!exists("server")) {
)
dates <- data.frame(
test <- as.character(ta$Date)
EN = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"),  
for(i in 1:nrow(dates)) {
FI = c("Tammi", "Helmi", "Maalis", "Huhti", "Touko", "Kesä", "Heinä", "Elo", "Syys", "Loka", "Marras", "Joulu")
test <- gsub(dates$EN[i], dates$FI[i], test)
)
for(i in 1:nrow(dates)) {
hours <- gsub(dates$EN[i], dates$FI[i], hours)
}
}
}
test <- as.POSIXct(strptime(test, format = "%Y-%b-%d %H:%M:%S"))
test <- test + as.difftime(as.character(ta$Time), format = "%H:%M")
head(test)
test <- data.frame(Time = test, Temperature = as.numeric(as.character(ta$Result)))
test <- test[!is.na(test$Temperature) , ]
test$Date <- as.POSIXct(strptime(format(test$Time, "%Y-%m-%d"), format = "%Y-%m-%d"))
test$Tempbin <- cut(test$Temperature + 2.3 , breaks = seq(-30, 33, 3)) # 2.3 is the average difference between Kuopio and Helsinki
dat <- aggregate(test["Temperature"], test["Date"], FUN = mean)
ggplot(test, aes(x = Time, y = Temperature))+geom_line() + geom_point(data = dat, aes(x = Date, y = Temperature, colour = "Daily mean"))
ggplot(test, aes(x = Time, y = Tempbin))+geom_point()


hours <- as.POSIXct(strptime(hours, format = "%Y-%b-%d %H:%M:%S"))
hours <- hours + as.difftime(as.character(ta$Time), format = "%H:%M")
# Adjust temperature data: 2.3 C is the average difference between Kuopio and Helsinki
hours <- data.frame(Time = hours, Result = as.numeric(as.character(ta$Result)) + 2.3)
hours <- hours[!is.na(hours$Result) , ]
hours$Date <- as.POSIXct(strptime(format(hours$Time, "%Y-%m-%d"), format = "%Y-%m-%d"))
hours <- hours[hours$Time >= as.POSIXct("2014-03-01 00:00:00") & hours$Time < as.POSIXct("2015-03-01 00:00:00") , ]
days <- aggregate(hours["Result"], hours["Date"], FUN = mean)
days$Temperature <- cut(days$Result, breaks = seq(-30, 33, 3))
temperatures <- Ovariable("temperature", data = aggregate(days["Result"], days["Temperature"], FUN = mean))
temperdays <- Ovariable("temperdays", data = aggregate(days["Result"], days["Temperature"], FUN = length))
objects.store(temperatures, temperdays)
cat("Objects temperature, temperdays stored.\n")
#ggplot(hours, aes(x = Time, y = Result))+geom_line() + geom_point(data = days, aes(x = Date, y = Result, colour = "Daily mean", size = 2))
#ggplot(hours, aes(x = Time, y = Result))+geom_point()
</rcode>
</rcode>


Revision as of 14:28, 8 July 2015


Many pieces of data on this page came originally from Building stock in Helsinki, worked by the Decision analysis and risk management 2015 course.

Question

How much is energy consumed and to what purposes in Helsinki?

Answer

Rationale

U values based on overall data

The total heat consumption by district-heated buildings is 6921.65 GWh in 2013 (see below). We can derive the total energy efficiency value expressed as W /m2 /K for floor area and temperature difference between indoors and outdoors. The typical energy efficiency calculations (using the so called U value) assume that outdoor 17 °C is thermoneutral and lower values require heating. The total floor area of district-heated buildings is 38990000 m2 in 2015 according to the Helsinki energy decision 2015 model. The annual average temperature in Helsinki is 4.8 °C [1] and during heating season Sep-May 1.4 C (Opasnet data). Therefore the energy efficiency value (approximate U value) is

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle U = \frac{6921.65 GWh/a /(24 h / d \times 365 d/a)}{38990000 m^2 (17 K - 4.8 K)} = 1.661 \frac{W}{m^2 K}}

The annual average ambient temperature is 2.5 °C in Kuopio (Ambient temperature in Urgenche cities). Therefore, we could use hourly temperature data from Kuopio if we add the temperature difference 2.3 K. Graphs of the data.

+ Show code

Energy parametres of buildings

Existing situation of important energy parametres in the building stock (%).
Property Residential buildings Description
Wall insulation 17 Default building data - Helsinki.xlsx and CyPT Data collection - To Jouni.pptx
High efficient glazing 35 Energy performance class A in building automation and www.siemens.com
Efficient lighting in baseline 1.4 Default building data - Helsinki.xlsx
Demand oriented lighting 26.9 Default building data - Helsinki.xlsx
Building Efficiency Monitoring
Building Remote Monitoring
Building Performance Optimization
Demand controlled ventilation 16.2 - 22.4 Default building data - Helsinki.xlsx: Non-residential, cell C35
Heat and Cold Recovery in ventilation 17.8
Efficient Motors
Building Automation BACS Class C
Building Automation BACS Class B
Building Automation BACS Class A
Room Automation HVAC 30 www.siemens.com
Room Automation HVAC + lighting
Building Automation HVAC + lighting + blinds 60 www.siemens.com

Description of parametres:

  • Wall Insulation: Building outside walls with a thermal transmission coefficient < 0.5(W/(m²*k)
  • High efficient glazing: Building outside windows with a thermal transmission coefficient < 1(W/(m²*k
  • Efficient lighting: Building with lighting technology with an efficiency of >75lumen/Watt
  • Demand oriented lighting: Buildings with a lighting regulation that controls the presents of users and/or the incident sun light to dim or shut off the lighting to save electricity.
  • Efficient Motors: Buildings that are equipped with pressure regulated variable speed drives for heating circulation pumps or ventilation drives.
  • Building Remote Monitoring: Buildings with metering and remote evaluation of energy demand, so that an professional engineer can evaluate the energy demand and the need for maintenance and improvement measures
  • Building Performance Optimization: Buildings with an optimization contract, where an professional engineer can start maintenance and improvement measures
  • Demand controlled ventilation: Buildings with a ventilation regulation that controls the air quality (e.B.CO2) to slow down or shut off the ventilation when it is not needed to save electricity.
  • Heat and Cold Recovery in ventilation: Buildings where the heat/cold of the exhaust air is recovered by a heat exchanger to precondition the fresh air to save energy for cooling/heating.
  • Room Automation HVAC: Buildings where the heating/ventilation/Air Condition demand is regulated to the demand of every single room to prevent energy demand for not used rooms.
Notes
Data from:

The numbers found are not reliable but most of the high technology buildings such as insulated 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 subtract 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 tech building areas with regard to 2 % renovation rate and rebuilding which is on going every year which should be added to the total amount.

Technical notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 1. Auxiliaries PPT.

Energy demand

Different energy sinks by building type (kWh /m2 /a). 0 = not known.
Energy type Use Residential Other Public Industry
Cooling Infiltration 0 0 0 0
Cooling Ventilation 0 0 0 9
Cooling Losses through walls through transmission 0 0 0 0
Cooling Heat input by solar radiation through windows 0 0 0 0
Cooling Losses through windows through transmission 0 0 0 0
Cooling Other effects (e.g. people, electrical Appliances) 0 0 0 0
Heating Infiltration 0 0 0 0
Heating Ventilation 29.19 27.56 27.22 27.22
Heating Walls 34.75 32.81 32.4 32.4
Heating Windows 15.49 14.63 14.44 14.44
Heating Floors 8.74 8.25 8.15 8.15
Heating Roofs 9.33 8.81 8.7 8.7
Heating Other 0 0 0 0
Heating Warm water 1.89 0 1.76 1.76
Electricity Lighting 2.82 20.05 49.68 8.67
Electricity Appliances 24.84 18.74 14.19 13
Electricity Ventilation 0 12.03 21.29 18.24
Electricity Other 0.56 23.69 56.77 51.76
Notes
  • Data from Climate policies Helsinki additional data
  • We went through the data mentioned above and also the files given in Climate policies Helsinki data but could not find the data for cooling, heating:infiltration, heating:other, heating:warm water:other and electricity:ventilation:residential.
  • Total estimated amount of energy needed for cooling 23504480 kWh/a (whole Helsinki). Based on [3] 14332 GWh/a for housing (total energy demand minus traffic 18 % [4] and [5] 2 % of total energy demand of housing used for cooling. Total floor area of Helsinki needed (table 1).

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

Efficiency increase

Changes in energy efficiency of different energy sinks.

  • Cooling 2 % /a
  • Warm water heating 1.1 % /a in residential buildings
  • General improvement: 0.6 % /a: Estimated 3 % of houses renovated/year and 20 % increase in energy efficiency when renovated. [6] Data apart from 0.6% values from Climate policies Helsinki additional data
  • The presentation about tables 4 and 5 PresentationHW9

Technical notes: Sheets 5_Input Residential, 6.0_Input Non Residential, 6.2_Input Public Admin. Priority 3. Auxiliaries Excel.

Heating parameters of buildings

Important energy parameters
Parameter Value Table Description
Efficiency increase of U values walls 1/a 0.019642857 u.factor (0.28-0.17)/0.28/(2010-1990)
Efficiency increase of U values windows 1/a 0.014285714 u.factor (1.4-1)/1.4/(2010-1990)
U value wall W/m2/K 0.17-0.28 u.factor values for buildings built 1990-2010
U value window W/m2/K 1.0-1.4 u.factor values for buildings built 1990-2010
G value % 70 sun.heat.absorption.parameters
Ratio of wall/effective area 0.647727273 surface.area year 2010: 114/176
Ratio of window/effective area 0.1 surface.area year 2010: 17.6/176
Notes

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

Values derived from Unit heat consumption of buildings in Finland (these could be used to update Table 6):

Consumption statistics

Total energy consumption in Helsinki in 2013 (GWh) [4]
Lämpökorjattu Lämpökorjaamaton
Kaukolämpö 6921,65 6461,00
Erillislämmitys 303,89 284,01
Sähkölämmitys 339,23 316, 65
Kulutussähkö 3988,10 3988,10
Henkilöautot 1294,06 1294,06
Muu tieliikenne 794,33 794,33
Junat 111,16 111,16
Laivat 432,12 432,12
Teollisuus ja työkoneet 147,60 147,60
Yhteensä 14332,14 13829,03

See also

Helsinki energy decision 2015
In English
Assessment Main page | Helsinki energy decision options 2015
Helsinki data Building stock in Helsinki | Helsinki energy production | Helsinki energy consumption | Energy use of buildings | Emission factors for burning processes | Prices of fuels in heat production | External cost
Models Building model | Energy balance | Health impact assessment | Economic impacts
Related assessments Climate change policies in Helsinki | Climate change policies and health in Kuopio | Climate change policies in Basel
In Finnish
Yhteenveto Helsingin energiapäätös 2015 | Helsingin energiapäätöksen vaihtoehdot 2015 | Helsingin energiapäätökseen liittyviä arvoja | Helsingin energiapäätös 2015.pptx

Keywords

References

  1. ↑ 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.
  2. ↑ Kragh, J., Laustsen, J. B., & Svendsen, S. (2008). Proposal for Energy Rating System of windows in EU. DTU Civil Engineering-Report R-201.
  3. ↑ http://www.ziegel.at/gbc-ziegelhandbuch/eng/ressourcen/energie/graue.htm
  4. ↑ Helsingin ympäristötilasto

Related files

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