ZEPHIR @PassiEXPO 2013

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The importance of Climatic Data Dr. Phys. Francesco Nesi Zero Energy and Passivhaus Institute for Research

Transcript of ZEPHIR @PassiEXPO 2013

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The importance of Climatic Data

Zero Energy and Passivhaus Institute for Research

Dr. Phys. Francesco Nesi

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The ZEPHIR Institute is the Italian iPHA Affiliate.

PH CertificationSolar modeling

PHPP, CEPH Training

Dynamical thermo-

hygrometrical simulations

Research

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The Climate sheet in PHPP 8

Among the first sheets, this stresses the importance of climatic data

Source: PHPP8

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The Climate sheet in PHPP 8

Let's take a closer look!

Average values for the different months of the year

Annual Heating/Cooling demand

Extreme values worst cases throughout the year (2 days)

Heating/Cooling Load

Source: PHPP8

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The climate data set

Month 1 2 3

Days 31 28 31

User data - Coredo MN7 Latitude: 46.3 Longitude °

Ambient temp -1.7 0.3 4.0North 12 16 25East 39 49 78

South 123 123 139West 41 51 84Global 53 72 121

Dew point -6.7 -5.7 -3.2Sky temp -17.2 -15.6 -12.3

Ambient temperature

• Dry bulb temperature• Easy to get• Available in all the

meteorological station

Source: PHPP8

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The climate data set

Month 1 2 3

Days 31 28 31

User data - Coredo MN7 Latitude: 46.3 Longitude °

Ambient temp -1.7 0.3 4.0North 12 16 25East 39 49 78

South 123 123 139West 41 51 84Global 53 72 121

Dew point -6.7 -5.7 -3.2Sky temp -17.2 -15.6 -12.3

Solar Radiation

• Global solar radiation on a horizontal surface and on a vertical surface oriented towards North, East, South and West

• More difficult to get• Usually available only the

global radiation on the horizontal plane

• The radiation on the vertical surfaces is not directly measured and has to be derived using theoretical models

(Erb's Model, Perez Model)

Reflected

Direct

Diffuse

Global = Diffuse + Direct + Reflected

Source: PHPP8

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The climate data set

Month 1 2 3

Days 31 28 31

User data - Coredo MN7 Latitude: 46.3 Longitude °

Ambient temp -1.7 0.3 4.0North 12 16 25East 39 49 78

South 123 123 139West 41 51 84Global 53 72 121

Dew point -6.7 -5.7 -3.2Sky temp -17.2 -15.6 -12.3

Dew Point temperature

• Required for dehumidification calculation and, if so, for calculation of sky temperature

• Calculated from relative humidity and ambient temperature

where

Source: PHPP8

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The climate data set

Month 1 2 3

Days 31 28 31

User data - Coredo MN7 Latitude: 46.3 Longitude °

Ambient temp -1.7 0.3 4.0North 12 16 25East 39 49 78

South 123 123 139West 41 51 84Global 53 72 121

Dew point -6.7 -5.7 -3.2Sky temp -17.2 -15.6 -12.3

Sky temperature

• Required for radiation correction in heat transfer calculations

• Can be estimated by PHPP from dew point temperature and ambient temperature

Source: PHPP8

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The climate data set

Heating Load data

Month Heating load Cooling load

Days Weather 1 Weather 2 Weather 1 Weather 2[IT] - Trentino-Trento Radiation: W/m² Radiation: W/m²

Ambient temp -7.1 -2.2 19.3 19.3North ... 16 13 71 71East 44 21 172 172

South 156 58 129 129West 49 26 145 145

Global 68 37 259 259

• Difficult to get, a dynamical simulation is needed

• Different from standard design data

• The heating loads data are represented by two set of temperatures and radiations corresponding to the most unfavorable days of the year Day 1

Cold, sunnyDay 2Moderate cold, cloudy

Source: PHPP8

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Window area [m²]

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Heat load

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Heat load

Heating Load Data• Depending on the south facing window area of the building the maximum

heating load occurs on Day 1 or on Day 2• The dependence on other parameters is linear

Day 1 Day 2

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Day 2

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Periode 2: mäßig kalt, bewölkt

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Periode 2: mäßig kalt, bewölkt

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Day 2

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Periode 2: mäßig kalt, bewölktDay 1 Cold and sunny Day 2 Moderate cold, cloudy

Source: CEPH Course

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Source: Bisanz, Carsten: "Heizlastauslegung im Niedrigenergie- und Passivhaus", diploma thesis 1998

Procedure: general idea

• Consider a typical Passivhaus with a given conductance • Identify the two unfavorable days (Day 1, Day 2) with a dynamical simulation• Determine the daily heating load in the two days with a dynamical simulation • Change the conductance new dynamical simulation • Calculate the external ambient temperature as follows

)/ • A similar procedure is adapted for the solar radiation data

Heating Load Data

Analogous procedure for cooling load data

Source: www.passivhaustagung.de

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Which data to use?

Possible source: GROUND STATIONS

Source: PCE Italia

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Which data to use?

Possible source: METONORM

Source: Meteonorm 7

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Which data to use?

Possible source: PASSIPEDIA [NASA]

Source: Passipedia

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Possible source: NATIONAL NORMS

Which data to use?

Source: UNI 10349:1994

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Location: Coredo, Trento, Italy (Lat. 46.347 Lon. 11.09 Elevation 815 m)

Example: influence of climatic data on a PHPP calculation

Source: Google Earth

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Example: influence of climatic data on a PHPP calculation

Specific building demands with reference to the treated floor area

Treated floor area 137.0 m² Requirements Fulfilled?*

Space heating Heating demand 15.8 kWh/(m2a) 15 kWh/(m²a) no

Heating load 11.8 W/m2 10 W/m² no

Space cooling Overall specif. space cooling demand kWh/(m2a) - -

Cooling load W/m2 - -

Frequency of overheating (> 25 °C) 13.0 % - -

Primary energyHeating, cooling,

auxiliary electricity,dehumidif ication, DHW,

lighting, electrical appliances kWh/(m2a) 120 kWh/(m²a)

DHW, space heating and auxiliary electricity kWh/(m2a) - -

Specific primary energy reduction through solar electricity 41.9 kWh/(m2a) - -

Airtightness Pressurization test result n50 0.4 1/h 0.6 1/h yes

* empty field: data missing; '-': no requirement

Specific building demands with reference to the treated floor area

Treated floor area 137.0 m² Requirements Fulfilled?*

Space heating Heating demand 5.9 kWh/(m2a) 15 kWh/(m²a) yes

Heating load 10.5 W/m2 10 W/m² -

Space cooling Overall specif. space cooling demand kWh/(m2a) - -

Cooling load W/m2 - -

Frequency of overheating (> 25 °C) 0.0 % - -

Primary energyHeating, cooling,

auxiliary electricity,dehumidif ication, DHW,

lighting, electrical appliances 44.8 kWh/(m2a) 120 kWh/(m²a) yes

DHW, space heating and auxiliary electricity 18.5 kWh/(m2a) - -

Specific primary energy reduction through solar electricity 48.8 kWh/(m2a) - -

Airtightness Pressurization test result n50 0.4 1/h 0.6 1/h yes

* empty field: data missing; '-': no requirement

Location: Coredo, Trento, Italy (Lat. 46.347 Lon. 11.09 Elevation 815 m)

• Source: Passipedia

• Source: Meteonorm 7

Source: PHPP8

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Example: influence of climatic data on a PHPP calculation

Large difference in the South Radiation, especially in the coldest month of the year

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Example: influence of climatic data on a PHPP calculation

Energy balance heating (annual method)

• Source: Meteonorm 7• Source: Passipedia

Solar gains: 28.1 vs 44.3

Source: PHPP8

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Location: Ancona, Italy (Lat. 43.616 Lon. 13.519 Elevation 34 m)

Example: influence of climatic data on a PHPP calculation

Source: Google Earth

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Specific building demands with reference to the treated floor area

Treated floor area 156.0 m² Requirements Fulfilled?*

Space heating Heating demand 9 kWh/(m2a) 15 kWh/(m²a) yes

Heating load 12 W/m2 10 W/m² -

Space cooling Overall specif. space cooling demand 16 kWh/(m2a) 15 kWh/(m²a) no

Cooling load 16 W/m2 10 W/m² no

Frequency of overheating (> 25 °C) % - -

Primary energyHeating, cooling,

auxiliary electricity,dehumidif ication, DHW,

lighting, electrical appliances 63 kWh/(m2a) 120 kWh/(m²a) yes

DHW, space heating and auxiliary electricity 21 kWh/(m2a) - -

Specific primary energy reduction through solar electricity 38 kWh/(m2a) - -

Airtightness Pressurization test result n50 0.2 1/h 0.6 1/h yes

* empty field: data missing; '-': no requirement

Specific building demands with reference to the treated floor area

Treated floor area 156.0 m² Requirements Fulfilled?*

Space heating Heating demand 8 kWh/(m2a) 15 kWh/(m²a) yes

Heating load 9 W/m2 10 W/m² yes

Space cooling Overall specif. space cooling demand 17 kWh/(m2a) 18 kWh/(m²a) yes

Cooling load 11 W/m2 - -

Frequency of overheating (> 25 °C) % - -

Primary energyHeating, cooling,

auxiliary electricity,dehumidif ication, DHW,

lighting, electrical appliances kWh/(m2a) 120 kWh/(m²a)

DHW, space heating and auxiliary electricity kWh/(m2a) - -

Specific primary energy reduction through solar electricity 40 kWh/(m2a) - -

Airtightness Pressurization test result n50 0.2 1/h 0.6 1/h yes

* empty field: data missing; '-': no requirement

Example: influence of climatic data on a PHPP calculationLocation: Ancona, Italy (Lat. 43.616 Lon. 13.519 Elevation 34 m)

• Source: Passipedia

• Source: Meteonorm 7

Source: PHPP8

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Example: influence of climatic data on a PHPP calculation

Difference in the dew point temperature, especially in summer

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Heating Demand 15 kWh/(m²a)

or: Heating Load 10 W/m²

Overall specif. space cooling demand

(cooling+dehumidification) 15 kWh/(m²a)

+ 0.3 W/(m²aK) * GHS

[if sensible cooling demand is lower than 15 Wh/(m²a)]

o: Cooling Load 10 W/m²

andOverall specif. space

cooling demand. 4 kWh/(m²aK) * Te

+ 2 * 0.3 W/(m²aK) * GHS- 75 kWh/(m²a) 45 kWh/(m²a)

+ 0.3 W/(m²aK) * GHS

Dry degree hours

Yearly average external temp.

Example: influence of climatic data on a PHPP calculationCertification Criteria

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Possible problemsPassipedia• Low resolution of the data

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Possible problemsMeteonorm• Heating and cooling loads not calculated according to the PHI prescription

One should perform a dynamical simulation for heating and cooling loads, this is not done by

Meteonorm

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Which data to use?One should not rely on a single data source!

Consider as many data sources as possible and make a comparison in order to verify the reliability of the climatic data!

Possible procedure

• collect all the available climatic data from different sources• convert the data in the PHPP format• compare the datasets and exclude possible wrong data, i.e data

which are numerically distant from the others: this can be done by means of a mathematical procedure called "outlier detection" Random error analysis Grubbs test Chauvanet's criterion

• consider the dataset which better reproduces the average value of the correct datasets

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1 2 3 4 5 6 7 8 9 10 11 120

50

100

150

200

250

300 MN7

CTI

Passipedia

UNI 10349 1

UNI 10349 2

Final Set

Month

Glo

bal R

adia

tion

Generation of a set of climatic data

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Conclusions

• Climatic data play a central role in the design of a Passivhaus• One cannot rely on a single source. Consider and compare as many

sources as possible

Outlook• We are working in order to validate new climatic data and enhance

the available climatic data for Italy• Ongoing collaboration with PHI and Meteonorm

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Let's do something better……

THANK YOU!

Dr. Phys. Francesco NesiDirector

Mobile: +39 346 6247437Email: [email protected]