SlideShare a Scribd company logo
1 of 12
Download to read offline
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
D6.4 S1.4
The Model for Energy Map
Calculation
„Building Energy Awareness”
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
1 - Problem analysis – laws
The scope of the model is to estimate energy performance of buildings (EP).
The model is based on European laws.
Main laws are:
EN 15217 indicates global indicators for the energy performance of whole building.
EN 15603 indicates a general framework for the assessment of overall energy use.
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
2 - Problem analysis - EP
In general, overall energy performance 𝐸𝑃𝑔𝑙 is calculated using the formula:
𝐸𝑃𝑔𝑙 = 𝐸𝑃𝑖 + 𝐸𝑃 𝐷𝐻𝑊
where Qℎ = Qℎ,𝑙𝑠 - γℎ,𝑔𝑛Qℎ,𝑔𝑛
STEPS :
1. Seasonal thermal energy
2. Annual Domestic Hot Water energy
3. For each terms calculate energy use in buildings referred to area.
𝐸𝑃𝑔𝑙 = 𝐸𝑃𝑖 + 𝐸𝑃 𝐷𝐻𝑊 + 𝐸𝑃𝑒 + 𝐸𝑃𝑖𝑙𝑙
In our model:
𝐸𝑃 =
(𝑄/𝐴 𝑟𝑖𝑓)
η 𝑠𝑦𝑠
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
3 - Heating energy request Qh
- HDD is the Heating Degree Day.
In general this value is calculated as accumulated differences between internal and
external temperature.
Energy need for heating 𝑄ℎ is given by EN 13790 «Energy performance of buildings:
Calculation of energy use for space heating and cooling ».
𝑸 𝒉 = 𝟎, 𝟎𝟐𝟒 ∙ 𝑯𝑫𝑫 ∙ (𝑯 𝑻 + 𝑯 𝑽 ) - 𝒇 𝒙 (𝑸 𝒔+𝑸𝒊)
( 20 [°C] – 5,2 [°C] ) x 174 [d/a] = 2575 [Kd/a]
T ExternalT Internal Heating days Heating Degree Day
- Ht is the heat transfer coefficient by transmission.
- Hv is the heat transfer coefficient by ventilation.
𝑯 𝑽 = 𝟎, 𝟑𝟒 ∙ 𝒏 ∙ 𝑽
Where V is volume of building and n the ventilation rate.
𝑯 𝑻 = (𝑨 𝒆𝒏𝒗,𝒊 ∙ 𝑼𝒊 ∙ 𝒃 𝒕𝒓,𝒊 ) + 𝚫𝑼 𝒕𝒃 ∙ ( 𝑨 𝒆𝒏𝒗,𝒊)
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
3 - Heating energy request Qh
- Qs is the solar heat load during heating season.
For example:
Qs = ( 0,9 x 0,75 ) x 𝐀 𝒘𝒊𝒏𝒅𝒐𝒘 x I
Non
perpendicular
Solar energy
transmittance
Energy need for heating 𝑄ℎ is given by EN 13790 «Energy performance of buildings:
Calculation of energy use for space heating and cooling ».
𝑸 𝒉 = 𝟎, 𝟎𝟐𝟒 ∙ 𝑯𝑫𝑫 ∙ (𝑯 𝑻 + 𝑯 𝑽 ) - 𝒇 𝒙 (𝑸 𝒔+𝑸𝒊)
Area
window
- Qi is the internal heat sources.
Irradiation
- fx is the gain utilization factor for heating.
𝑸 𝑰 = ( 𝜽𝒊𝒏𝒕 𝐱 𝐀 𝒇𝒍𝒐𝒐𝒓 𝐱 𝒉 ) ∶ 𝟏𝟎𝟎𝟎
Internal heat
sources
per unit area Area
Heating
hours
fx = 0,95
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
3 - Heating energy request Qh
Geometric values
Si (Envelope element area)
𝐀 𝒘𝒊𝒏𝒅𝒐𝒘 (Window area)
Volume
Afloor (Area)
Thermal values
Ui (U-value)
𝚫𝑼 𝒕𝒃 (Thermal bridge)
Climatic Data
I (Irradiation)
T External
Heating days
The model need several parameter for each building.
How
to
calculate
them ?
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
3 - Heating energy request Qh
Buildings characteristics with age after
1900: data are estimated by TABULA
project.
By Tabula are estimated also climatic data.
Some geometric values (such as external perimeter and floor area)
are estimated by geometric shape file.
Window area and thermal proprieties depend on buildings age, typologies and region.
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
3 - Heating energy request Qh
Buildings characteristics with age
before 1900: data from historical
analysis of Ferrara University.
From wall material (stone or brick) and
average width of one building type, it is
possible calculate wall width for each
building.
So we can calculate Heating energy request Qh
Some geometric values (such as external perimeter and floor area)
are estimated by geometric shape file.
Window area and thermal proprieties depend on buildings age, typologies and region.
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
4 – Domestic Hot Water
𝐐 𝑫𝑯𝑾 = ( 𝟏, 𝟏𝟔𝟐 x 𝑽 𝑾 x ( 𝜽 𝑯 − 𝜽 𝑪 ) x 𝟑𝟔𝟓
Volume
DHW
Energy need for domestic hot water 𝑄 𝐷𝐻𝑊 is given by EN 15316 series:
“Heating systems in buildings - Method for calculation of system energy requirements
and system efficiencies”
temperature
hot water
Days
temperature
inlet
Where:
• Volume DHW is calculated directly on floor area [ l / day ]
• Temperature hot water is 40 °C
• Temperature inlet water is 15 °C
𝑽 𝑾 = a x 𝑨 𝒇𝒍𝒐𝒐𝒓
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
The energy model validation requires some considerations:
• Not all energy certifications are based on the entire building;
• Building age in the model isn’t always as real age;
• We don’t know real refurbishment.
5 – Validation - Trento
Difference between EPi – building with similar S/V
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
User can choose the location of the plan
Use variable (with value 0 or 1) for the control of dispersant surfaces
The energy model validation requires some considerations:
• Not all energy certifications are based on the entire building
6 – Use
𝑸 𝒉 = 𝟎, 𝟎𝟐𝟒 ∙ 𝑯𝑫𝑫 ∙ (𝑯 𝑻 + 𝑯 𝑽 ) - 𝒇 𝒙 (𝑸 𝒔+𝑸𝒊)
𝑯 𝑻 = (𝜶𝒊 ∙ 𝑨 𝒆𝒏𝒗,𝒊 ∙ 𝑼𝒊 ∙ 𝒃 𝒕𝒓,𝒊 ) + 𝚫𝑼 𝒕𝒃 ∙ (𝜶𝒊 ∙ 𝑨 𝒆𝒏𝒗,𝒊)
• Building age in the model isn’t always as real age
• We don’t know real refurbishment
User can choose the data for simulation
www.sunshineproject.eu
SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161)
Credits
For more training material and courses visit http://www.sunshineproject.eu/solutions/training
or contact us directly at training@sunshineproject.eu
Source:www.unionegeometri.com
Thank you!
Marco Berti
Fondazione Graphitech
marco.berti@graphitech.it

More Related Content

What's hot

UrbaWind, a Computational Fluid Dynamics tool to predict wind resource in urb...
UrbaWind, a Computational Fluid Dynamics tool to predict wind resource in urb...UrbaWind, a Computational Fluid Dynamics tool to predict wind resource in urb...
UrbaWind, a Computational Fluid Dynamics tool to predict wind resource in urb...
Stephane Meteodyn
 

What's hot (20)

S.1.3 INSPIRE Directive
S.1.3 INSPIRE DirectiveS.1.3 INSPIRE Directive
S.1.3 INSPIRE Directive
 
S.2.f Specifications for Data Ingestion via Green Button
S.2.f Specifications for Data Ingestion via Green ButtonS.2.f Specifications for Data Ingestion via Green Button
S.2.f Specifications for Data Ingestion via Green Button
 
S.2.h Meter Data Management Service
S.2.h Meter Data Management ServiceS.2.h Meter Data Management Service
S.2.h Meter Data Management Service
 
Servizio Gestione Flussi Dati Energetici Edifici
Servizio Gestione Flussi Dati Energetici EdificiServizio Gestione Flussi Dati Energetici Edifici
Servizio Gestione Flussi Dati Energetici Edifici
 
S.2.g Meter and Sensor Data Management Service
S.2.g Meter and Sensor Data Management ServiceS.2.g Meter and Sensor Data Management Service
S.2.g Meter and Sensor Data Management Service
 
S.1.b Building Energy Pre Certification Service
S.1.b Building Energy Pre Certification ServiceS.1.b Building Energy Pre Certification Service
S.1.b Building Energy Pre Certification Service
 
S.2.i Suggestion Service
S.2.i Suggestion ServiceS.2.i Suggestion Service
S.2.i Suggestion Service
 
SUNSHINE Project: Romain Nouvel, Jean Marie Bahu
SUNSHINE Project: Romain Nouvel, Jean Marie BahuSUNSHINE Project: Romain Nouvel, Jean Marie Bahu
SUNSHINE Project: Romain Nouvel, Jean Marie Bahu
 
S.1.a Data Model for Energy Map Data Collection
S.1.a Data Model for Energy Map Data CollectionS.1.a Data Model for Energy Map Data Collection
S.1.a Data Model for Energy Map Data Collection
 
SUNSHINE Project: Francesco Pignatelli, Maria Teresa Borzacchiello
SUNSHINE Project: Francesco Pignatelli, Maria Teresa BorzacchielloSUNSHINE Project: Francesco Pignatelli, Maria Teresa Borzacchiello
SUNSHINE Project: Francesco Pignatelli, Maria Teresa Borzacchiello
 
S.2.e Specifications for Data Ingestion via Sunshine FTP
S.2.e Specifications for Data Ingestion via Sunshine FTPS.2.e Specifications for Data Ingestion via Sunshine FTP
S.2.e Specifications for Data Ingestion via Sunshine FTP
 
Energy efficiency in buildings
Energy efficiency in buildingsEnergy efficiency in buildings
Energy efficiency in buildings
 
Nice Cote d'Azur: a leading smart city - IRIS case study
Nice Cote d'Azur: a leading smart city - IRIS case studyNice Cote d'Azur: a leading smart city - IRIS case study
Nice Cote d'Azur: a leading smart city - IRIS case study
 
Vehicle to Grid ecosystem at scale: Utrecht case study
Vehicle to Grid ecosystem at scale: Utrecht case studyVehicle to Grid ecosystem at scale: Utrecht case study
Vehicle to Grid ecosystem at scale: Utrecht case study
 
Fire out pitch deck
Fire out pitch deckFire out pitch deck
Fire out pitch deck
 
Machine Learning with Earth Observation Imagery
Machine Learning with Earth Observation ImageryMachine Learning with Earth Observation Imagery
Machine Learning with Earth Observation Imagery
 
UrbaWind, a Computational Fluid Dynamics tool to predict wind resource in urb...
UrbaWind, a Computational Fluid Dynamics tool to predict wind resource in urb...UrbaWind, a Computational Fluid Dynamics tool to predict wind resource in urb...
UrbaWind, a Computational Fluid Dynamics tool to predict wind resource in urb...
 
Niagara Dashboard Application
Niagara Dashboard ApplicationNiagara Dashboard Application
Niagara Dashboard Application
 
Small Scale Photovoltaic Installations - Use of RETScreen Software
Small Scale Photovoltaic Installations - Use of RETScreen SoftwareSmall Scale Photovoltaic Installations - Use of RETScreen Software
Small Scale Photovoltaic Installations - Use of RETScreen Software
 
GIS Based Power Distribution System: A Case study for the Junagadh City
GIS Based Power Distribution System: A Case study for the Junagadh CityGIS Based Power Distribution System: A Case study for the Junagadh City
GIS Based Power Distribution System: A Case study for the Junagadh City
 

Viewers also liked

Nutrition in icu
Nutrition in icuNutrition in icu
Nutrition in icu
Siti Azila
 
Human body systems
Human body systemsHuman body systems
Human body systems
rlinde
 
MAJOR ORGANS IN HUMAN BODY
MAJOR ORGANS IN HUMAN BODYMAJOR ORGANS IN HUMAN BODY
MAJOR ORGANS IN HUMAN BODY
Wan Norazlina
 

Viewers also liked (9)

Calculation of energy_needs
Calculation of energy_needsCalculation of energy_needs
Calculation of energy_needs
 
Anatomy Body PowerPoint Templates
Anatomy Body PowerPoint TemplatesAnatomy Body PowerPoint Templates
Anatomy Body PowerPoint Templates
 
Calculation of energy_needs
Calculation of energy_needsCalculation of energy_needs
Calculation of energy_needs
 
Nutrition (espen & aspen guidelines)
Nutrition (espen & aspen guidelines)Nutrition (espen & aspen guidelines)
Nutrition (espen & aspen guidelines)
 
Nutrition in icu
Nutrition in icuNutrition in icu
Nutrition in icu
 
The Human Body...A Fun Quiz
The Human Body...A Fun QuizThe Human Body...A Fun Quiz
The Human Body...A Fun Quiz
 
Human body systems
Human body systemsHuman body systems
Human body systems
 
Anatomy and Physiology; Introduction to the human body
Anatomy and Physiology; Introduction to the human bodyAnatomy and Physiology; Introduction to the human body
Anatomy and Physiology; Introduction to the human body
 
MAJOR ORGANS IN HUMAN BODY
MAJOR ORGANS IN HUMAN BODYMAJOR ORGANS IN HUMAN BODY
MAJOR ORGANS IN HUMAN BODY
 

Similar to S.1.4 Model for Energy Map Calculation

Photovoltaic Project Analysis Using RETScreen software
Photovoltaic Project Analysis Using RETScreen softwarePhotovoltaic Project Analysis Using RETScreen software
Photovoltaic Project Analysis Using RETScreen software
Leonardo ENERGY
 

Similar to S.1.4 Model for Energy Map Calculation (20)

Photovoltaic Project Analysis Using RETScreen software
Photovoltaic Project Analysis Using RETScreen softwarePhotovoltaic Project Analysis Using RETScreen software
Photovoltaic Project Analysis Using RETScreen software
 
Energy Efficiency Survey of the Ganado Advocates Head Office
Energy Efficiency Survey of the Ganado Advocates Head OfficeEnergy Efficiency Survey of the Ganado Advocates Head Office
Energy Efficiency Survey of the Ganado Advocates Head Office
 
3.1-General-energy-efficiency-measures-in-buildings-rev.pptx
3.1-General-energy-efficiency-measures-in-buildings-rev.pptx3.1-General-energy-efficiency-measures-in-buildings-rev.pptx
3.1-General-energy-efficiency-measures-in-buildings-rev.pptx
 
Lessons Learned from Meter Calibrated Energy Simulations of Multi-Unit Reside...
Lessons Learned from Meter Calibrated Energy Simulations of Multi-Unit Reside...Lessons Learned from Meter Calibrated Energy Simulations of Multi-Unit Reside...
Lessons Learned from Meter Calibrated Energy Simulations of Multi-Unit Reside...
 
Energy Simulation of High-Rise Residential Buildings: Lessons Learned
Energy Simulation of High-Rise Residential Buildings: Lessons LearnedEnergy Simulation of High-Rise Residential Buildings: Lessons Learned
Energy Simulation of High-Rise Residential Buildings: Lessons Learned
 
"Emprendimiento del futuro ligados con la eficiencia energética y accesibilid...
"Emprendimiento del futuro ligados con la eficiencia energética y accesibilid..."Emprendimiento del futuro ligados con la eficiencia energética y accesibilid...
"Emprendimiento del futuro ligados con la eficiencia energética y accesibilid...
 
AS3 cop-21_US
AS3 cop-21_USAS3 cop-21_US
AS3 cop-21_US
 
Building Energy Simulation project by using eQuest
Building Energy Simulation project by using eQuestBuilding Energy Simulation project by using eQuest
Building Energy Simulation project by using eQuest
 
Energy auditing and energy efficiency indicators
Energy auditing and energy efficiency indicatorsEnergy auditing and energy efficiency indicators
Energy auditing and energy efficiency indicators
 
IRJET- Experimental Model Design and Simulation of Air Conditioning System fo...
IRJET- Experimental Model Design and Simulation of Air Conditioning System fo...IRJET- Experimental Model Design and Simulation of Air Conditioning System fo...
IRJET- Experimental Model Design and Simulation of Air Conditioning System fo...
 
Smart Grids, Energy Efficiency and Renewable Energy Sources in urban areas: t...
Smart Grids, Energy Efficiency and Renewable Energy Sources in urban areas: t...Smart Grids, Energy Efficiency and Renewable Energy Sources in urban areas: t...
Smart Grids, Energy Efficiency and Renewable Energy Sources in urban areas: t...
 
Energy efficiency
Energy efficiencyEnergy efficiency
Energy efficiency
 
Modeling and performance evaluation of energy efficient buildings envelope us...
Modeling and performance evaluation of energy efficient buildings envelope us...Modeling and performance evaluation of energy efficient buildings envelope us...
Modeling and performance evaluation of energy efficient buildings envelope us...
 
Architectural Environmental Control part 2.pdf
Architectural Environmental Control part 2.pdfArchitectural Environmental Control part 2.pdf
Architectural Environmental Control part 2.pdf
 
Modelling Natural Ventilation in IES-VE: Case studies & Research Outlook
Modelling Natural Ventilation in IES-VE: Case studies & Research OutlookModelling Natural Ventilation in IES-VE: Case studies & Research Outlook
Modelling Natural Ventilation in IES-VE: Case studies & Research Outlook
 
Modelling Natural Ventilation in IES-VE: Case studies & Research Outlook
Modelling Natural Ventilation in IES-VE: Case studies & Research OutlookModelling Natural Ventilation in IES-VE: Case studies & Research Outlook
Modelling Natural Ventilation in IES-VE: Case studies & Research Outlook
 
Energy modeling 101 (public)
Energy modeling 101 (public)Energy modeling 101 (public)
Energy modeling 101 (public)
 
Team IES - 2016 ASHRAE Lowdown Showdown
Team IES - 2016 ASHRAE Lowdown ShowdownTeam IES - 2016 ASHRAE Lowdown Showdown
Team IES - 2016 ASHRAE Lowdown Showdown
 
Leandro Madrazo, ARC Engineering and Architecture La Salle, Barcelona, Spain.
Leandro Madrazo, ARC Engineering and Architecture La Salle, Barcelona, Spain.Leandro Madrazo, ARC Engineering and Architecture La Salle, Barcelona, Spain.
Leandro Madrazo, ARC Engineering and Architecture La Salle, Barcelona, Spain.
 
006 160719 urban environment analysis for new and existing neighborhood
006 160719 urban environment analysis for new and existing neighborhood006 160719 urban environment analysis for new and existing neighborhood
006 160719 urban environment analysis for new and existing neighborhood
 

More from SUNSHINEProject (8)

Sunshine lamia greek native language
Sunshine lamia greek native languageSunshine lamia greek native language
Sunshine lamia greek native language
 
S.3.k Security Layer
S.3.k Security LayerS.3.k Security Layer
S.3.k Security Layer
 
SUNSHINE Project: Bart delathouwer
SUNSHINE Project: Bart delathouwerSUNSHINE Project: Bart delathouwer
SUNSHINE Project: Bart delathouwer
 
SUNSHINE Project: Paolo Conci
SUNSHINE Project: Paolo ConciSUNSHINE Project: Paolo Conci
SUNSHINE Project: Paolo Conci
 
Sunshine Project: Energy Maps Trenta
Sunshine Project: Energy Maps TrentaSunshine Project: Energy Maps Trenta
Sunshine Project: Energy Maps Trenta
 
S.2.4 Validation Activities for Scenario 2 (case Ferrara)
S.2.4 Validation Activities for Scenario 2 (case Ferrara)S.2.4 Validation Activities for Scenario 2 (case Ferrara)
S.2.4 Validation Activities for Scenario 2 (case Ferrara)
 
SUNSHINE Project - Scenario 2 (HR)
SUNSHINE Project - Scenario 2 (HR)SUNSHINE Project - Scenario 2 (HR)
SUNSHINE Project - Scenario 2 (HR)
 
SUNSHINE Project - Map4data App (IT)
SUNSHINE Project - Map4data App (IT)SUNSHINE Project - Map4data App (IT)
SUNSHINE Project - Map4data App (IT)
 

Recently uploaded

Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
 

Recently uploaded (20)

How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 

S.1.4 Model for Energy Map Calculation

  • 1. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) D6.4 S1.4 The Model for Energy Map Calculation „Building Energy Awareness”
  • 2. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) 1 - Problem analysis – laws The scope of the model is to estimate energy performance of buildings (EP). The model is based on European laws. Main laws are: EN 15217 indicates global indicators for the energy performance of whole building. EN 15603 indicates a general framework for the assessment of overall energy use.
  • 3. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) 2 - Problem analysis - EP In general, overall energy performance 𝐸𝑃𝑔𝑙 is calculated using the formula: 𝐸𝑃𝑔𝑙 = 𝐸𝑃𝑖 + 𝐸𝑃 𝐷𝐻𝑊 where Qℎ = Qℎ,𝑙𝑠 - γℎ,𝑔𝑛Qℎ,𝑔𝑛 STEPS : 1. Seasonal thermal energy 2. Annual Domestic Hot Water energy 3. For each terms calculate energy use in buildings referred to area. 𝐸𝑃𝑔𝑙 = 𝐸𝑃𝑖 + 𝐸𝑃 𝐷𝐻𝑊 + 𝐸𝑃𝑒 + 𝐸𝑃𝑖𝑙𝑙 In our model: 𝐸𝑃 = (𝑄/𝐴 𝑟𝑖𝑓) η 𝑠𝑦𝑠
  • 4. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) 3 - Heating energy request Qh - HDD is the Heating Degree Day. In general this value is calculated as accumulated differences between internal and external temperature. Energy need for heating 𝑄ℎ is given by EN 13790 «Energy performance of buildings: Calculation of energy use for space heating and cooling ». 𝑸 𝒉 = 𝟎, 𝟎𝟐𝟒 ∙ 𝑯𝑫𝑫 ∙ (𝑯 𝑻 + 𝑯 𝑽 ) - 𝒇 𝒙 (𝑸 𝒔+𝑸𝒊) ( 20 [°C] – 5,2 [°C] ) x 174 [d/a] = 2575 [Kd/a] T ExternalT Internal Heating days Heating Degree Day - Ht is the heat transfer coefficient by transmission. - Hv is the heat transfer coefficient by ventilation. 𝑯 𝑽 = 𝟎, 𝟑𝟒 ∙ 𝒏 ∙ 𝑽 Where V is volume of building and n the ventilation rate. 𝑯 𝑻 = (𝑨 𝒆𝒏𝒗,𝒊 ∙ 𝑼𝒊 ∙ 𝒃 𝒕𝒓,𝒊 ) + 𝚫𝑼 𝒕𝒃 ∙ ( 𝑨 𝒆𝒏𝒗,𝒊)
  • 5. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) 3 - Heating energy request Qh - Qs is the solar heat load during heating season. For example: Qs = ( 0,9 x 0,75 ) x 𝐀 𝒘𝒊𝒏𝒅𝒐𝒘 x I Non perpendicular Solar energy transmittance Energy need for heating 𝑄ℎ is given by EN 13790 «Energy performance of buildings: Calculation of energy use for space heating and cooling ». 𝑸 𝒉 = 𝟎, 𝟎𝟐𝟒 ∙ 𝑯𝑫𝑫 ∙ (𝑯 𝑻 + 𝑯 𝑽 ) - 𝒇 𝒙 (𝑸 𝒔+𝑸𝒊) Area window - Qi is the internal heat sources. Irradiation - fx is the gain utilization factor for heating. 𝑸 𝑰 = ( 𝜽𝒊𝒏𝒕 𝐱 𝐀 𝒇𝒍𝒐𝒐𝒓 𝐱 𝒉 ) ∶ 𝟏𝟎𝟎𝟎 Internal heat sources per unit area Area Heating hours fx = 0,95
  • 6. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) 3 - Heating energy request Qh Geometric values Si (Envelope element area) 𝐀 𝒘𝒊𝒏𝒅𝒐𝒘 (Window area) Volume Afloor (Area) Thermal values Ui (U-value) 𝚫𝑼 𝒕𝒃 (Thermal bridge) Climatic Data I (Irradiation) T External Heating days The model need several parameter for each building. How to calculate them ?
  • 7. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) 3 - Heating energy request Qh Buildings characteristics with age after 1900: data are estimated by TABULA project. By Tabula are estimated also climatic data. Some geometric values (such as external perimeter and floor area) are estimated by geometric shape file. Window area and thermal proprieties depend on buildings age, typologies and region.
  • 8. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) 3 - Heating energy request Qh Buildings characteristics with age before 1900: data from historical analysis of Ferrara University. From wall material (stone or brick) and average width of one building type, it is possible calculate wall width for each building. So we can calculate Heating energy request Qh Some geometric values (such as external perimeter and floor area) are estimated by geometric shape file. Window area and thermal proprieties depend on buildings age, typologies and region.
  • 9. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) 4 – Domestic Hot Water 𝐐 𝑫𝑯𝑾 = ( 𝟏, 𝟏𝟔𝟐 x 𝑽 𝑾 x ( 𝜽 𝑯 − 𝜽 𝑪 ) x 𝟑𝟔𝟓 Volume DHW Energy need for domestic hot water 𝑄 𝐷𝐻𝑊 is given by EN 15316 series: “Heating systems in buildings - Method for calculation of system energy requirements and system efficiencies” temperature hot water Days temperature inlet Where: • Volume DHW is calculated directly on floor area [ l / day ] • Temperature hot water is 40 °C • Temperature inlet water is 15 °C 𝑽 𝑾 = a x 𝑨 𝒇𝒍𝒐𝒐𝒓
  • 10. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) The energy model validation requires some considerations: • Not all energy certifications are based on the entire building; • Building age in the model isn’t always as real age; • We don’t know real refurbishment. 5 – Validation - Trento Difference between EPi – building with similar S/V
  • 11. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) User can choose the location of the plan Use variable (with value 0 or 1) for the control of dispersant surfaces The energy model validation requires some considerations: • Not all energy certifications are based on the entire building 6 – Use 𝑸 𝒉 = 𝟎, 𝟎𝟐𝟒 ∙ 𝑯𝑫𝑫 ∙ (𝑯 𝑻 + 𝑯 𝑽 ) - 𝒇 𝒙 (𝑸 𝒔+𝑸𝒊) 𝑯 𝑻 = (𝜶𝒊 ∙ 𝑨 𝒆𝒏𝒗,𝒊 ∙ 𝑼𝒊 ∙ 𝒃 𝒕𝒓,𝒊 ) + 𝚫𝑼 𝒕𝒃 ∙ (𝜶𝒊 ∙ 𝑨 𝒆𝒏𝒗,𝒊) • Building age in the model isn’t always as real age • We don’t know real refurbishment User can choose the data for simulation
  • 12. www.sunshineproject.eu SUNSHINE - Smart UrbaN ServIces for Higher eNergy Efficiency (GA no: 325161) Credits For more training material and courses visit http://www.sunshineproject.eu/solutions/training or contact us directly at training@sunshineproject.eu Source:www.unionegeometri.com Thank you! Marco Berti Fondazione Graphitech marco.berti@graphitech.it