Statistical Building Energy Model from Data Collection, Place-Based Assessment to Sustainable Scenarios for the City of Milan
Abstract
:1. Introduction
2. The City of Milan
3. Materials and Methods
3.1. Data Collection
3.1.1. Energy Performance Certificates Database
3.2. Pre-Processing and Geographical Database Creation
3.3. Statistical Modeling of Buildings’ Energy Consumption
- (1)
- data collection from EPCs’ database, technical maps and ISTAT data;
- (2)
- pre-processing phase to calculate and add other variables that could influence the energy consumption;
- (3)
- the database of EPCs was subdivided into two datasets of non-retrofitted and retrofitted buildings considering the motivation of the release of the EPCs;
- (4)
- evaluation of the more energy-related variables with Pearson’s correlation;
- (5)
- definition of subsets of consumption data considering the more energy-related variables; for each subset, the frequency distribution of consumption data was verified with the Kolmogorov–Smirnov and/or Chi-squared tests;
- (6)
- identification and exclusion of outliers from EPCs’ database together with the null data;
- (7)
- formulation of the multilinear regression for the evaluation of the energy performance of non-retrofitted and retrofitted buildings;
- (8)
- multicollinearity and residual analysis;
- (9)
- definitive multilinear regression models.
- from the technical map of the city of Milan: the building use, footprint area, height or number of floors, gross volume, net heated surface, surface-to-volume ratio S/V, period of construction;
- from the National Institute of Statistics ISTAT for population and buildings: the number of inhabitants and families, age, nationality, working level, buildings’ property, level of maintenance, occupation profile, period of construction class.
Description of Alternative Machine Learning Models
4. Results and Discussion
4.1. Energy Performance Certificates (EPC) Analysis
4.2. GIS-Based Energy Modeling and Sensitivity Analysis
4.3. Statistical Energy Consumption Model
- -
- EPnren is the primary energy performance index in kWh/m2/year considering non-renewable sources for space heating (H) and domestic hot water (DHW);
- -
- EC is the energy consumption (or delivered energy) in kWh/year;
- -
- fp,nren is the conversion factor of delivered energy into primary energy (for natural gas, this is 1.05);
- -
- m2 is the net heated area of a building.
4.4. Evaluation of Solar Irradiation and Energy Production with Roof-Integrated Technologies
- Flat solar thermal collector STC with optical efficiency of 77.5% and heat loss coefficients k1 = 4.35 and k2 = 0.01.
- Photovoltaic PV panel in monocrystalline silicon with an efficiency of 23%.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
SELECT a.*, b.id || ‘, len:’ || round(st_length(st_intersection(a.geom, b.geom)), 4) AS “neighbor_info” FROM “building layer” AS a, “building layer” AS b WHERE st_intersects(a.geom, b.geom) AND a.id <> b.id ORDER BY a.id ASC |
- where;
- o
- round(st_length(st_intersection(a.geom, b.geom)), 4): calculates the length of the intersection between the two geometries (a and b), with a decimal number.
- o
- AS “neighbor_info”: renames the result of the series as “neighbor_info” and assigns it to a new column in the result set.
- o
- “building layer”: this should be replaced with the actual name of the building layer. It is the table for using the query, and we use the aliases a and b to reference it twice for the self-join operation.
- o
- AS a, AS b: These are table aliases. AS is used to give a table a temporary name (a and b in this case) so that it can be used as a reference to the same table multiple times in the query.
- o
- st_intersects(a.geom, b.geom): checks if the geometries of a and b intersect.
- o
- a.id <> b.id: ensures that the same polygon is not compared with itself by checking that the id of a is not equal to the id of b. This prevents self-comparisons.
- o
- a.id ASC: Sorts the result set by the id column of table a in ascending order.
References
- Jasiūnas, J.; Lund, P.D.; Mikkola, J. Energy system resilience—A review. Renew. Sustain. Energy Rev. 2021, 150, 111476. [Google Scholar] [CrossRef]
- United Nations: Department of Economic and Social Affairs. UN-Energy Plan of Action, May 2022. Available online: https://un-energy.org/wp-content/uploads/2022/05/UN-Energy-Plan-of-Action-towards-2025-2May2022.pdf (accessed on 7 August 2022).
- Hasselqvist, H.; Renström, S.; Strömberg, H.; Håkansson, M. Household energy resilience: Shifting perspectives to reveal opportunities for renewable energy futures in affluent contexts. Energy Res. Soc. Sci. 2022, 88, 102498. [Google Scholar] [CrossRef]
- Eurostat: Final Energy Consumption by Sector, EU-27, 2018 (% of Total, Based on Tonnes of Oil Equivalent). Available online: https://ec.europa.eu/eurostat/ (accessed on 20 July 2023).
- McKeen, P.; Fung, A. The effect of building aspect ratio on energy efficiency: A case study for multi-unit residential buildings in Canada. Buildings 2014, 4, 336–354. [Google Scholar] [CrossRef]
- Duran, A.; Iseri, O.K.; Meral Akgul, C.; Kalkan, S.; Gursel Dino, I. Compiling Open Datasets to Improve Urban Building Energy Models with Occupancy and Layout Data. In Proceedings of the 27th CAADRIA Conference, Sydney, Australia, 9–15 April 2022. [Google Scholar] [CrossRef]
- Malhotra, A.; Bischof, J.; Nichersu, A.; Häfele, K.H.; Exenberger, J.; Sood, D.; Allan, J.; Frisch, J.; van Treeck, C.; O’Donnell, J.; et al. Information modelling for urban building energy simulation—A taxonomic review. Build. Environ. 2022, 208, 108552. [Google Scholar] [CrossRef]
- Sun, Y.; Haghighat, F.; Fung, B.C. A review of the-state-of-the-art in data-driven approaches for building energy prediction. Energy Build. 2020, 221, 110022. [Google Scholar] [CrossRef]
- Database CENED+2—Energy Certification of Buildings in the Municipality of Milan. Available online: https://dati.comune.milano.it/dataset/ds623_database_cened2__certificazione_energetica_degli_edifici_nel (accessed on 20 July 2023).
- ISTAT. Quattordicesimo Censimento Generale della Popolazione e delle Abitazioni. 2001. Available online: http://dawinci.istat.it (accessed on 7 August 2023).
- UNI 10349:2016; Heating and Cooling of Buildings—Climatic Data. Italian Standardization Entity UNI: Rome, Italy, 2016.
- Covenant of Mayors, Sustainable Energy Action Plan 2018. Available online: https://www.covenantofmayors.eu/about/covenant-community/signatories/action-plan.html?scity_id=11832 (accessed on 10 June 2023).
- ARPA Lombardia, Atmospheric Emissions Inventory INEMAR (INventario EMissioni ARia). Available online: https://www.inemar.eu/xwiki/bin/view/InemarDatiWeb/Risultati+Regionali (accessed on 10 June 2023). (In Italian).
- City of Milan, Air and Climate Plan. Available online: https://www.comune.milano.it/piano-aria-clima (accessed on 10 June 2023). (In Italian).
- A New Air and Climate Plan for Milan. Available online: https://eit.europa.eu/news-events/news/new-air-and-climate-plan-milan (accessed on 10 June 2022).
- Padovani, C.; Salvaggio, C. Strategia per la sostenibilità ambientale e resilienza urbana nel Pgt della Città di Milano: Il Piano aria clima. In Pianificazione Urbana e Territoriale dalla Lezione di Giampiero Vigliano alle Prospettive del Green New Deal—Urbanistica Dossier; Istituto Nazionale di Urbanistica: Rome, Italy, 2022; Volume 27, pp. 117–119. ISBN 9788876032417. Available online: http://www.inuedizioni.com/it/prodotti/rivista/n-027-urbanistica-dossier (accessed on 20 July 2023). (In Italian)
- EU Project: Cities on Power. Available online: https://keep.eu/programmes/119/2007-2013-Central-Europe/ (accessed on 20 July 2023).
- Mutani, G.; Todeschi, V. GIS-based urban energy modelling and energy efficiency scenarios using the energy performance certificate database. Energy Effic. 2020, 14, 47. [Google Scholar] [CrossRef]
- Mutani, G.; Todeschi, V.; Beltramino, S. Energy consumption models at urban scale to measure energy resilience, Sustainability—Bridging the Gap: The Measure of Urban Resilience. Sustainability 2020, 12, 5678. [Google Scholar] [CrossRef]
- Energia Lombardia. Available online: https://www.energialombardia.eu/energia-e-territorio#:~:text=Per%20quanto%20attiene%20i%20consumi,0%2C1%20tep%20procapite (accessed on 10 June 2023). (In Italian).
- ARERA (Regulatory Authority for Energy, Networks and Environment), Analysis of Consumption of Domestic Customers. Available online: https://www.arera.it/it/dati/mr/mr_consumiele.htm (accessed on 10 June 2023). (In Italian).
- Ghoshchi, A.; Zahedi, R.; Pour, Z.; Ahmadi, A. Machine Learning Theory in Building Energy Modeling and Optimization: A Bibliometric Analysis. J. Mod. Green Energy 2022, 1, 4. [Google Scholar] [CrossRef]
- “PIANO ARIA E CLIMA” del Comune di Milano. Available online: https://www.comune.milano.it/documents/20126/430903598/sub+Allegato+4+-+Mitigazione.pdf/b672b7bd-201e-dc47-fb03-e7453faae745?t=1652093322345 (accessed on 10 June 2023). (In Italian).
- Mutani, G.; Todeschi, V. Optimization of Costs and Self-Sufficiency for Roof Integrated Photovoltaic Technologies on Residential Buildings. Energies 2021, 14, 4018. [Google Scholar] [CrossRef]
- Huld, T.; Müller, R.; Gambardella, A. A new solar radiation database for estimating PV performance in Europe and Africa. Sol. Energy 2012, 86, 1803–1815. [Google Scholar] [CrossRef]
- Davila, C.C.; Reinhart, C.F.; Bemis, J.L. Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets. Energy 2016, 117 Pt 1, 237–250. [Google Scholar] [CrossRef]
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Tavg °C | 4.0 | 7.1 | 10.6 | 13.4 | 19.4 | 22.8 | 24.5 | 24.3 | 19.8 | 14.1 | 7.5 | 3.5 |
I, kWh/m2 | 42.2 | 53.4 | 101.6 | 133.3 | 163.6 | 190.8 | 200.6 | 164.5 | 126.7 | 68.9 | 35.8 | 31.0 |
Typology | Input Data | Typology | Source | License |
---|---|---|---|---|
Energy consumption models and energy-related variables | ||||
Built-up environment/Urban morphology/Type of users | Regional-Municipal data by Participatory Mapping” | OpenStreetMap | https://download.geofabrik.de/ (accessed on 20 July 2023) https://download.geofabrik.de/europe/italy.html (accessed on 20 July 2023) https://osmit-estratti.wmcloud.org/ (accessed on 20 July 2023) (for Region and Municipality) | Open Data Commons Open Database License (ODbL) |
Public Buildings | Treasure Dept | Dati immobili al 31/12/2018—MEF Dipartimento del Tesoro | CC-BY 4.0 | |
Territorial Geo-topographic Database (BDTRE) | Italian Regions | Piedmont Region: https://www.geoportale.piemonte.it/cms/bdtre/modalita-di-pubblicazione-e-fruizione (accessed on 20 July 2023); City of Turin: http://geoportale.comune.torino.it/web/cartografia/cartografia-scarico (accessed on 20 July 2023); Lombardy Region: https://www.geoportale.regione.lombardia.it/download-ricerca (accessed on 20 July 2023); City of Milan: https://geoportale.comune.milano.it/sit/open-data/ (accessed on 20 July 2023) | CC-BY 4.0 International | |
Digital Terrain/Elevation/Surface Model (DTM, DEM, DSM) for Italy | SINAnet—DEM (20 m) | ISPRA: http://www.sinanet.isprambiente.it/it/sia-ispra/download-mais/dem20/view (accessed on 20 July 2023) | IODL License https://www.dati.gov.it/content/italian-open-data-license-v20 (accessed on 20 July 2023) | |
TINITALY—DTM (10 m) | Istituto Nazionale di Geofisica e Vulcanologia (INGV, National Institute of Geophysics and Volcanology): https://data.ingv.it/dataset/185#additional-metadata (accessed on 20 July 2023) | CC BY | ||
DTM 20 m | IGM- Military Geographical Institute: http://www.pcn.minambiente.it/mattm/catalogo-metadati/ (accessed on 20 July 2023); http://www.pcn.minambiente.it/mattm/visualizzazione-metadati/?keyword=digital+terrain+model&rid=local (accessed on 20 July 2023) Free GIS data for Italy: https://freegisdata.org/place/106881/ (accessed on 20 July 2023) (information only) | Creative Commons Attribution 4.0 International | ||
DTM 20–40–75 m | ISPRA Higher Institute for Environmental Protection and Research: http://dati.isprambiente.it/ (accessed on 20 July 2023) | Licence | ||
DSM 1 m (Italy) | Ministry of Ecological Transition (MITE): http://www.pcn.minambiente.it/mattm/visualizzazione-metadati/?keyword=dsm&rid=local (accessed on 20 July 2023) | and open data | ||
Satellite images (Landsat 8) | Raster 30 m × 30 m | https://earthexplorer.usgs.gov/ (accessed on 20 July 2023) https://www.usgs.gov/coastal-changes-and-impacts/gmted2010 (accessed on 20 July 2023) | No restrictions, all GMTED2010 data products are available | |
Orthophotos | Color orthophoto AGEA 2009–2012 | Ministry of Ecological Transition (MITE): http://www.pcn.minambiente.it/mattm/visualizzazione-metadati/?keyword=ortofoto&rid=local&paged_e=1 (accessed on 20 July 2023) | Open data | |
Color and b&w digital Orthophoto | Free GIS data for Italy: https://freegisdata.org/place/106881/ (accessed on 20 July 2023) (information only) | Open data | ||
Land use/cover | SINAnet—CORINE Land Cover (Italy) | 1990: https://groupware.sinanet.isprambiente.it/uso-copertura-e-consumo-di-suolo/library/copertura-del-suolo/corine-land-cover/corine-land-cover-1990 (accessed on 20 July 2023) 2000: https://groupware.sinanet.isprambiente.it/uso-copertura-e-consumo-di-suolo/library/copertura-del-suolo/corine-land-cover/corine-land-cover-2000 (accessed on 20 July 2023) 2018: https://groupware.sinanet.isprambiente.it/uso-copertura-e-consumo-di-suolo/library/copertura-del-suolo/corine-land-cover/clc2018_shapefile (accessed on 20 July 2023) | IODL License | |
SINAnet—Land taking | https://groupware.sinanet.isprambiente.it/uso-copertura-e-consumo-di-suolo/library/consumo-di-suolo (accessed on 20 July 2023) | CC BY SA 3.0 IT | ||
European statistics about Environment & Energy and SDGs | EUROSTAT | https://ec.europa.eu/eurostat/data/database (accessed on 20 July 2023) | Open Data | |
Socio-economic characteristics | Population, housing, industry and services census section data Industry and services | National Institute of Statistics ISTAT—Territory and census data | ISTAT: https://www.istat.it/it/archivio/104317 (accessed on 20 July 2023) “Basi Territoriali e Variabili Censuarie” (new data will be available at: https://www.istat.it/it/archivio/6789 (accessed on 20 July 2023)) Census of population and housing and of industry and services (txt-xls-csv) 2011 http://dati.istat.it/Index.aspx?DataSetCode=DCIS_POPRES1 (accessed on 20 July 2023) | Open Data |
Climate data | Air temperature, relative humidity, wind velocity, solar irradiation, heating degree days (HDD) | Climate data | JRC: https://re.jrc.ec.europa.eu/pvg_tools/en/tools.html (accessed on 20 July 2023) Italian Standard UNI 10349-1, -2, -3:2016 (Italian Standardization Body) | PVGIS © European Communities, 2001–2021 |
EnergyPlus: https://energyplus.net/weather (accessed on 20 July 2023) | Open data: https://energyplus.net/weather/sources (accessed on 20 July 2023) | |||
Energy consumption data | Sustainable Energy and Climate Action Plan (SEAP-SECAP) | Covenant of Mayors—Europe | https://eu-mayors.ec.europa.eu/en/home (accessed on 20 July 2023) https://www.covenantofmayors.eu/plans-and-actions/action-plans.html (accessed on 20 July 2023) | Website coordinated by the Covenant of Mayors Office, European Commission. |
Residential user profile for electricity (Province and Regional; 2021–2022) | ARERA (Regulatory Authority for Energy, Networks and Environment) | https://www.arera.it/it/dati/mr/mr_consumiele.htm (accessed on 20 July 2023) | Open Data | |
Measurements | ‘e-distribuzione’ portal | https://private.e-distribuzione.it/PortaleClienti/PED_SiteLogin (accessed on 20 July 2023) | Confidential-private information | |
Surveys/monitoring of electrical energy | TERNA (Electricity Transmission National Grid) Driving Energy | https://www.terna.it/it/sistema-elettrico/transparency-report/total-load (accessed on 20 July 2023) (per bidding-zone) | Open Data Copyright TERNA | |
Energy costs (withdrawing costs of electricity and fuels) | ARERA, Consumer Protection Centre and WTRG Economics | ARERA (Regulatory Authority for Energy Networks and The Environment) https://www.arera.it/it/dati/gp27new.htm (accessed on 20 July 2023) (natural gas) https://www.arera.it/it/dati/eep35.htm (accessed on 20 July 2023) (electricity) and https://www.consumer.bz.it/it/confronto-prezzi-combustibili-riscaldamento-alto-adige (accessed on 20 July 2023) (fuels for space heating) and http://www.wtrg.com/daily/crudeoilprice.html (accessed on 20 July 2023) (crude oil) | Open Data Copyright ARERA Copyright 1999–2022 by James L. Williams | |
Thermal plants/systems | Registry for thermal plants/systems—Lombardy Region | https://www.dati.lombardia.it/Energia/Catasto-Unico-Regionale-Impianti-Termici-Impianti-/d7i4-7rpy (accessed on 20 July 2023) https://dati.comune.milano.it/dataset/ds598_catasto_unico_regionale_impianti_termici__impianti_targati_n (accessed on 20 July 2023) | Open Data CC | |
Energy Performance Certificates (EPCs) database of buildings | CENED and CENED+2 Lombardy Region Piedmont Region | https://dati.comune.milano.it/dataset/ds604_cened__certificazione_energetica_degli_edifici_nel_comune_di (accessed on 20 July 2023) https://dati.comune.milano.it/dataset/ds623_database_cened2__certificazione_energetica_degli_edifici_nel (accessed on 20 July 2023) https://www.geoportale.piemonte.it/geonetwork/srv/ita/catalog.search#/metadata/r_piemon:42f87394-4ec6-4764-bdf8-57bc12d4e0f2 (accessed on 20 July 2023) | Open Data CC BY | |
Energy production models | ||||
Energy production data | Surveys/monitoring of electrical energy | TERNA (Electricity Transmission National Grid) Driving Energy | https://www.terna.it/it/sistema-elettrico/transparency-report/actual-generation (accessed on 20 July 2023) (per primary energy source) | Open Data Copyright TERNA |
Revenue for electricity production in the national grid (per zone and month, Prezzo Zonale Orario PO) | Energy Services Management GSE | https://www.gse.it/servizi-per-te/fotovoltaico/ritiro-dedicato/regolazione-economica-del-servizio (accessed on 20 July 2023) https://www.gse.it/servizi-per-te/fotovoltaico/ritiro-dedicato/documenti (accessed on 20 July 2023) | Open Data Copyright GSE | |
Type of technological system and power installed | ‘Atlaimpianti’ (Energy Services Management GSE) online portal | https://www.gse.it/dati-e-scenari/atlaimpianti (accessed on 20 July 2023) | Database pursuant to art. 1 L. 22/4/1941 n. 633, as amended by Leg. Decree 6/5/1999 n. 169 | |
Global Atlas for Renewable Energy | IRENA | International Renewable Energy Agency IRENA: https://www.irena.org/globalatlas (accessed on 20 July 2023) | Open Data Copyright IRENA | |
Thermal plants/systems | Regional Land Registry for thermal plants/systems—Lombardy Region | https://www.dati.lombardia.it/Energia/Catasto-Unico-Regionale-Impianti-Termici-Impianti-/d7i4-7rpy (accessed on 20 July 2023) https://dati.comune.milano.it/dataset/ds598_catasto_unico_regionale_impianti_termici__impianti_targati_n (accessed on 20 July 2023) | Open Data CC 0 | |
Energy prices by time and zone of the electricity produced and fed into the grid (electricity) | Energy Services Management GSE S.p.A. | https://www.gse.it/servizi-per-te/fotovoltaico/ritiro-dedicato/documenti (accessed on 20 July 2023) under “Parola Chiave” type “Ritiro Dedicato”— under “Tipologia” select “Altri Contenuti” | Open Data CC BY NC SA | |
Energy potential production models | ||||
Built-up environment/Urban morphology | Regional-Municipal data by “Participatory Mapping” | OpenStreetMap | https://download.geofabrik.de/ (accessed on 20 July 2023) https://download.geofabrik.de/europe/italy.html (accessed on 20 July 2023) https://osmit-estratti.wmcloud.org/ (accessed on 20 July 2023) (Region and Municipality) | Open Data Commons Open Database License (ODbL) |
Territorial Geo-topographic Database (BDTRE) | Italian Regions (in Italian) | e.g., Piedmont Region: https://www.geoportale.piemonte.it/cms/bdtre/modalita-di-pubblicazione-e-fruizione (accessed on 20 July 2023) | CC-BY 4.0 International | |
Digital Terrain/Surface/Elevation Model (DEM, DSM, DTM) Digital Elevation Model | SINAnet—DEM (20 m) (Italy) | ISPRA: http://www.sinanet.isprambiente.it/it/sia-ispra/download-mais/dem20/view (accessed on 20 July 2023) | IODL License https://www.dati.gov.it/content/italian-open-data-license-v20 (accessed on 20 July 2023) | |
DEM 10 m (Italy) | Istituto Nazionale di Geofisica e Vulcanologia (INGV): https://data.ingv.it/dataset/185#additional-metadata (accessed on 20 July 2023) | CC_BY | ||
DSM 1 m (Italy) | Ministero della Transizione Ecologica: http://www.pcn.minambiente.it/mattm/visualizzazione-metadati/?keyword=dsm&rid=local (accessed on 20 July 2023) | Open data | ||
Renewable energy sources | Solar radiation data | Photovoltaic GIS, JRC | https://re.jrc.ec.europa.eu/pvg_tools/it/tools.html (accessed on 20 July 2023) | PVGIS © European Communities, 2001–2021 |
Italian Solar Atlas ENEA | National Agency for New Technologies, Energy and Sustainable Economic Development: http://www.solaritaly.enea.it/ (accessed on 20 July 2023) | CC-BY-SA | ||
Linke Turbidity Factor Worldwide | SODA: https://www.soda-pro.com/help/general-knowledge/linke-turbidity-factor (accessed on 20 July 2023) | CAMS License Agreement and Privacy Statement with License Agreement for SoDa | ||
COP—Solar Portal for Torino Metropolitan City | Cities On Power Project: https://keep.eu/projects/5551/Cities-on-Power-EN/ (accessed on 20 July 2023) http://energia.sistemapiemonte.it/ittb-torino (accessed on 20 July 2023) | Open Data | ||
Biomass data | Italian Biomass Atlas ENEA (2017–2020) | National Agency for New Technologies, Energy and Sustainable Economic Development Biomass Atlas: http://atlantebiomasse.brindisi.enea.it/atlantebiomasse/ (accessed on 20 July 2023) | Copyright 2016 ENEA—Atlante delle Biomasse | |
National organization ‘Risi’ (Rices) | http://www.enterisi.it/servizi/Menu/dinamica.aspx?idSezione=17505&idArea=17548&idCat=17552&ID=17552&TipoElemento=categoria (accessed on 20 July 2023) | Legal notes | ||
Waste to energy (i.e., waste production per capita) | Waste institute (ISPRA) | ISPRA Higher Institute for Environmental Protection and Research: https://www.catasto-rifiuti.isprambiente.it/index.php?pg=&width=1093&height=615 (accessed on 20 July 2023) | CC-BY | |
Wind data | Italian Wind Atlas RSE | http://atlanteeolico.rse-web.it/ (accessed on 20 July 2023) | Copyright RSE S.p.A. | |
Global Wind Atlas 3.1 | https://globalwindatlas.info/en/area/Italy (accessed on 20 July 2023) | DTU Wind Energy | ||
Meteorological institute ‘CMWF Reading | ECMWF European Centre for Medium-Range Weather Forecasts: https://www.ecmwf.int/en/forecasts (accessed on 20 July 2023) | For researchers licence for non-commercial use | ||
Sea velocity, temperature, salinity, wave height, mean wave period and wave energy | ENEA | National Agency for New Technologies, Energy and Sustainable Economic Development https://climaweb.casaccia.enea.it/WW3MED/details.php (accessed on 20 July 2023) https://giotto.casaccia.enea.it/forecasts/ (accessed on 20 July 2023) | Property of ENEA (SSPT-MET-CLIM Lab) | |
Global Atlas for Renewable Energy | International Renewable Energy Agency IRENA | https://www.irena.org/globalatlas (accessed on 20 July 2023) | Open Data Copyright IRENA | |
Constraints | Soil protection repository | National Repository of interventions for Soil Protection (ReNDiS) | http://www.datiopen.it/it/catalogo-opendata/file-shp (accessed on 20 July 2023) | The Italian portal of Open Data: property of Sistemi Territoriali S.r.l. © 2012 |
Land use and hazards | Landslides, floods, avalanches, seismic, fires, ect | ISPRA Higher Institute for Environmental Protection and Research: http://dati.isprambiente.it/ (accessed on 20 July 2023) Free GIS data for Italy: https://freegisdata.org/place/106881/ (accessed on 20 July 2023) (information only) | Creative Commons Attribution 4.0 International Open data | |
Maps showing soil protection | Energy Services Management GSE S.p.A. | https://www.gse.it/documenti_site/Documenti%20GSE/Studi%20e%20scenari/Regolazione%20regionale%20FER%2031_12_2020.pdf (accessed on 20 July 2023) | Gestore dei Servizi Energetici—GSE S.p.A |
Energy Classes | Period of Construction | ||||||
---|---|---|---|---|---|---|---|
<1930 | 1930–1945 | 1946–1960 | 1961–1976 | 1977–1992 | 1993–2006 | >2006 | |
A4 | 39 | 40 | 51 | 36 | 50 | 7 | 3045 |
A3 | 91 | 57 | 90 | 38 | 23 | 19 | 3580 |
A2 | 117 | 160 | 288 | 192 | 95 | 63 | 3436 |
A1 | 178 | 160 | 494 | 444 | 170 | 68 | 2701 |
B | 227 | 273 | 552 | 698 | 217 | 277 | 1638 |
C | 711 | 897 | 1600 | 2098 | 541 | 1035 | 1479 |
D | 2979 | 3209 | 5549 | 6982 | 1445 | 2322 | 1553 |
E | 5537 | 6275 | 10,115 | 12,756 | 2279 | 2181 | 1092 |
F | 7871 | 9831 | 16,510 | 19,949 | 2676 | 1375 | 752 |
G | 8121 | 10,507 | 17,346 | 19,626 | 2178 | 560 | 418 |
Motivation of EPC Release | |||
---|---|---|---|
Energy Class and Average EPgl,nren | New Buildings | Retrofitted Buildings | Sale and Lease of Buildings |
A4 28 kWh/m2/y | 2414 | 395 | 309 |
A3 48 kWh/m2/y | 2673 | 626 | 281 |
A2 68 kWh/m2/y | 2274 | 1003 | 584 |
A1 79 kWh/m2/y | 1291 | 1421 | 1093 |
B 99 kWh/m2/y | 132 | 1057 | 2037 |
C 118 kWh/m2/y | 39 | 1520 | 5882 |
D 139 kWh/m2/y | 44 | 2638 | 18,686 |
E 158 kWh/m2/y | 55 | 2777 | 33,268 |
F 189 kWh/m2/y | 84 | 3294 | 49,617 |
G 277 kWh/m2/y | 99 | 2054 | 52,109 |
Energy Performance EP (kWh/m2/y) | EPH,nd Total | EPgl,nren Total | EPgl,nren Non-Retrofitted | EPgl,nren Retrofitted |
---|---|---|---|---|
N. of dwellings | 189,756 | 189,756 | 163,866 | 25,890 |
Average EP | 98.64 | 187.86 | 199.52 | 114.05 |
Median EP | 83.80 | 167.70 | 88.73 | 90.42 |
Pearson’s Correlation | EPgl,nren | EPH,nd |
---|---|---|
Period of construction | −42.3% | −41.6% |
S/V ratio (m2/m3) | 38.6% | 46.9% |
CO2 emissions | 78.4% | 63.9% |
Potential energy performance 1 | 45.5% | 44.8% |
Potential energy performance 2 | 79.1% | 72.5% |
Year of installation of space heating system 1 | −31.6% | −26.5% |
Year of installation of domestic hot water system 2 | −27.0% | −25.6% |
Foreigners’ persons residing in Italy over 54 years old | 6.2% | 5.9% |
Resident illiterate population | 4.5% | 4.4% |
Resident families with 1 member | 5.1% | 2.5% |
Population aged 15 and over housewives | 5.5% | 2.6% |
Period of Construction | Classes of S/V (m2/m3) | Number of Buildings | ||||
---|---|---|---|---|---|---|
<0.2 | 0.2–0.5 | 0.51–0.8 | 0.81–1.1 | >1.1 | ||
<1930 & 1930–1945 | 2140 (80) | 14,133 (738) | 5941 (437) | 924 (45) | 193 (8) | 23,332 (1308) |
1946–1960 | 489 (23) | 11,419 (210) | 13,462 (141) | 2003 (19) | 355 (2) | 27,727 (395) |
1961–1970 | 1261 (23) | 19,588 (283) | 6645 (147) | 741 (21) | 73 (6) | 28,309 (480) |
1971–1980 | 686 (11) | 11,293 (178) | 3884 (91) | 428 (11) | 43 (3) | 16,333 (294) |
1981–1990 | 397 (4) | 3648 (200) | 1539 (111) | 178 (0) | 22 (0) | 5784 (315) |
1991–2000 & 2001–2005 | 152 (0) | 2504 (8) | 1271 (10) | 188 (0) | 12 (1) | 4127 (19) |
>2006 | 193 (37) | 7430 (692) | 4174 (400) | 514 (57) | 46 (1) | 12,357 (1187) |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
D/G | 0.54 | 0.51 | 0.46 | 0.44 | 0.43 | 0.40 | 0.35 | 0.38 | 0.42 | 0.52 | 0.59 | 0.59 |
TL | 2.74 | 2.98 | 3.5 | 3.91 | 3.85 | 3.9 | 3.67 | 3.54 | 3.45 | 3.4 | 3.02 | 2.72 |
District No. | Energy-Use for Space Heating (kWht) | Energy-Use for Domestic Hot Water (kWht) | Energy-Use for Electricity (kWhe) | Energy Production with PV (kWhe) | Energy Production with STC (kWht) | Main Period of Construction |
---|---|---|---|---|---|---|
1 | 966,262,459 | 70,622,702 | 159,492,639 | 134,778,376 | 124,714,482 | Before 1919 |
2 | 1,320,954,361 | 105,323,577 | 237,860,272 | 92,261,859 | 85,416,638 | 1919–1945 |
3 | 1,297,445,750 | 103,449,166 | 233,627,147 | 94,075,251 | 87,083,751 | 1919–1945 |
4 | 1,352,919,902 | 113,009,059 | 255,216,982.6 | 87,760,731 | 81,229,151 | 1946–1960 |
5 | 1,066,383,008 | 89,074,704 | 201,164,202.8 | 69,278,538 | 64,136,604 | 1946–1960 |
6 | 1,288,344,047 | 107,615,054 | 243,035,290 | 69,681,565 | 64,524,164 | 1946–1960 |
7 | 1,495,087,787 | 124,884,306 | 282,035,761 | 93,955,781 | 86,975,366 | 1946–1960 |
8 | 1,594,529,076 | 133,190,612 | 300,794,525 | 101,970,262 | 94,403,965 | 1946–1960 |
9 | 1,472,543,840 | 129,151,277 | 291,672,186 | 101,399,317 | 93,906,003 | 1961–1970 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mutani, G.; Alehasin, M.; Usta, Y.; Fiermonte, F.; Mariano, A. Statistical Building Energy Model from Data Collection, Place-Based Assessment to Sustainable Scenarios for the City of Milan. Sustainability 2023, 15, 14921. https://doi.org/10.3390/su152014921
Mutani G, Alehasin M, Usta Y, Fiermonte F, Mariano A. Statistical Building Energy Model from Data Collection, Place-Based Assessment to Sustainable Scenarios for the City of Milan. Sustainability. 2023; 15(20):14921. https://doi.org/10.3390/su152014921
Chicago/Turabian StyleMutani, Guglielmina, Maryam Alehasin, Yasemin Usta, Francesco Fiermonte, and Angelo Mariano. 2023. "Statistical Building Energy Model from Data Collection, Place-Based Assessment to Sustainable Scenarios for the City of Milan" Sustainability 15, no. 20: 14921. https://doi.org/10.3390/su152014921