On the Impact of Climate Change on Building Energy Consumptions: A Meta-Analysis
Abstract
:1. Introduction
- What are the main implications of climate change on building energy consumptions according to the existing literature? To what extent do these implications differ between the studies?
- Since several research methodologies can be pointed out, are there any correlations between methodological inputs and research outcomes? In particular, the effects of heating degree-days, cooling degree-days, reference period, future time slices, and emission scenarios (summarized by means of CO2 concentrations) on the energy consumption variation (heating, cooling, and total) were investigated by statistical techniques.
2. Review Methodology
2.1. Studies Selection
- To capture articles related to climate change: future weather data; future climate data; climate variables; weather files; weather data; future projections; weather forecasting; climate change impact; climate change; changing climate; future climate condition; future scenarios.
- To capture articles related to buildings: buildings.
- To capture articles related to energy consumption: energy demand, energy consumption, energy performance, performance assessment.
2.2. Data Extraction
2.3. Meta-Analysis
- Data preparation
- Studies combination
- Exploration of heterogeneity
3. Overview of Studies
3.1. Geographical Overview
3.2. Building Typologies Overview
3.3. Methods Overview
4. Results of Meta-Analysis
4.1. Findings Overview
4.2. Statistical Analysis
5. Management Implications
- Mitigate global and local climate change.
- Increase of energy savings, improving building performance considering future weather conditions, and designing buildings with the minimum possible cooling needs.
- Improve the efficiency of mechanical air conditioning and alternative cooling technologies.
6. Research Limitation and Future Prospect
7. Conclusions
- From a geographical point of view, the spread of the studies does not appear to be homogeneous across the planet, but rather a preponderance of investigations was identified in Europe, far-east Asia, and the eastern United States, with a special emphasis on climate zone C (65% of studies). Further research should be conducted by encompassing other climate zones, since climate change does not affect the planet uniformly.
- The literature on the impacts of climate change still appears to be related to specific building types such as residential (40% of studies) and office buildings (26%), neglecting other building typologies. Nevertheless, since climate change adaptation measures will be needed in the coming years, regardless of the efforts to tackle global warming, further building types need to be studied and specific adaptation strategies identified. Indeed, each building type presents specific characteristics that do not allow it to be compared with the other, and adaptation measures should be tailored to ensure the best performance.
- Several considerations can be highlighted about the employed methodologies. Firstly, most studies still adopt as current climate files climate, files based on weather data observed before 1990 (37% of studies), thus obsolete and not suitable for representing the current climate which is already affected by climate change. Accordingly, the availability of weather files based on more recent data representative of the actual climate is essential to conducting reliable assessments. Secondly, the reviewed studies appear to be largely based on the SRES emission scenarios (54% of studies), which are now outdated. As impact assessments are strongly influenced by the emission scenario selected to generate future weather files, the spread of investigations based on the new IPCC scenarios is desirable. Finally, regarding downscaling techniques, the imposed offset method (which includes the morphing method) is undoubtedly the most widespread approach, accounting for more than half of the manuscripts (61% of studies), while the use of the stochastic and dynamical methods is found to be still limited. Given the high level of uncertainty in predictive analyses, further studies involving not a single approach, but rather the use of different methodologies should be conducted.
- Climate change is expected to be responsible for a deep change in the energy consumption of buildings. Indeed, according to the analyses carried out—which include a sample of 1671 data collected from the manuscripts-, the increase in temperatures will globally lead to: (i) a reduction in heating consumptions from −12.6% (2020) to −47.5% (2080); (ii) an increase in cooling consumptions from +28.8% (2020) to +60.9% (2080); (iii) a growth in total consumptions from +2.6% (2020) to +12% (2080). Clearly, these overall results are influenced by the different climate zones involved, which are affected by climate change to different extents. Climate zone A seems to suffer the greatest rise in energy consumption, while zone D appears to be the least affected.
- The statistical analysis of the data collected from the reviewed manuscripts confirmed that impact analyses on the building energy consumptions lead to extremely disparate results, with a high level of heterogeneity that does not allow us to identify a synthetic combined effect. This variability depends on the climate zone, the building typology, and the methodology adopted. The attempt to find a relationship between the energy consumption variation and HDDs, CDDs, reference period, CO2 concentration, did not result in the identification of strong correlations between the parameters. Thereby, two moderate linear correlations were identified. The former was found between the heating consumption variation and HDDs, which appear to be linked by a moderate positive linear correlation, because, as HDDs increase, there is a lower reduction in heating consumptions. The latter was found between the total consumption and HDDs. Indeed, the increase in total consumption is higher in areas characterized by smaller values of HDDs, decreasing progressively as the HDDs increase, until reaching negative values.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Ref | Building Type | Location | Climate Zone | Reference Period | HDDs | CDDs | Emission Scenario | Downscaling | Future Time Slice | Target | Year |
---|---|---|---|---|---|---|---|---|---|---|---|
[75] | Office, residential | New York, | Dfa | 2020 | N.A. | N.A. | SSP126, SSP245, SSP370, SSP585 | new XAI model | 2100 | Incremental cooling consumption | 2021 |
San Antonio | Cfa | ||||||||||
[17] | Office | Hanoi | Cfa | 1961–1990 | 166 | 2070 | RCP4.5, RCP8.5 | Morphing | 2056–2075 2080–2099 | Yearly EC | 2021 |
Da Nang | Afm | 0 | 2862 | ||||||||
Kuala Lumpur | Af | 0 | 3065 | ||||||||
Bangkok | Aw | 0 | 3536 | ||||||||
[70] | Supermarket | London | Cfb | 1984–2013 | 2866 | 32 | A1B, A1F, B1 | Modified morphing | 2050, 2080 | Yearly EC | 2021 |
[102] | Office | Canberra | Cfb | 1982–1999 | 2120 | 195 | A2 | Morphing | 2080 | Yearly EU | 2021 |
Brisbane | 329 | 1061 | |||||||||
[30] | University | Reading | Cfb | 1961–1990 | 3185 | 453 | A1B, A1F | NA | 2030, 2050, 2080 | H EC | 2021 |
[78] | Office | Chengdu | Cwa | 2010–2017 | 1456 | 929 | RCP8.5 | Hybrid | 2095 | ED (kWh/m2) | 2021 |
Kathmandu | 1027 | 911 | |||||||||
Hanoi | 188 | 2339 | |||||||||
Islamabad | 829 | 2223 | |||||||||
Lucknow | 362 | 2733 | |||||||||
Zhengzhou | 2267 | 1052 | |||||||||
[79] | Residential | Rome | Csa | 1982–1999 | 1444 | 649 | RCP8.5, A2 | Morphing Stochastic Dynamical | 2050 | H and C net energy needs | 2021 |
[84] | Office | Montreal | Dfb | 2020 | N.A. | N.A. | RCP2.6, RCP4.5, RCP6.0, RCP8.5 | Hybrid classification-regression model | 2050 | ED | 2020 |
[103] | Residential | Malaga | Csa | 1961–1990 | 863 | 818 | B1, B2, A2, A1F1 | Morphing | 2020, 2025, 2030, 2050, 2080 | Primary EC | 2020 |
[104] | Residential | New York | Cfa | 1958 | N.A. | N.A. | +1.5 °C | Offset method | 2017, 2100 | Primary EC | 2020 |
[35] | Residential, hospital, healthcare, restaurant, hotel, office, retail, school, warehouse. | Toronto | Dfb | 1959–1989 | 4089 | 232 | A2, RPC8.5 | Morphing | 2050 | Yearly EU | 2020 |
[105] | University | Gainesville | Cfa | 2018 | N.A. | N.A. | N.A. | Dynamical | 2041, 2063, 2057 | ED | 2020 |
[65] | Residential | Hong Kong | Cwa | 1979–2003 | 202 | 2064 | RCP4.5, RCP8.5 | Morphing | 2035, 2065, 2090 | C demand | 2020 |
[68] | Residential | Madrid | Csa | 1982–1999 CTE | 1965 | 628 | Recorded data | Recorded data | 2008–2017 | Energy needs | 2020 |
[106] | Residential | Istanbul | Csa | 2010 | N.A. | N.A. | A2 | Stochastic | 2030 | H and C EC | 2020 |
[11] | Residential | Fresno | Csa | 1991–2005 | 1275 | 1238 | RCP4.5 | Morphing | 2026–2045 2056–2075 2080–2099 | Net energy | 2020 |
Riverside | Cfa | 909 | 710 | ||||||||
San Francesco | Csb | 1557 | 22 | ||||||||
[21] | Residential | Aberdeen | Cfb | 1961–1990 | 3719 | 1 | A2 | Morphing | 2050 | Yearly EC | 2020 |
Belfast | Cfb | 3371 | 2 | ||||||||
Berlin | Dfb | 3471 | 124 | ||||||||
Bordeaux | Cfb | 2169 | 248 | ||||||||
Clermont | Dfc | 2729 | 175 | ||||||||
Cluj-Napoca | Dfb | 3573 | 120 | ||||||||
Copenhagen | Dfb | 3687 | 40 | ||||||||
Göteborg | Dfb | 4005 | 29 | ||||||||
Granada | Bsk | 2105 | 353 | ||||||||
London | Cfb | 3131 | 44 | ||||||||
Milan | Cfa | 2682 | 379 | ||||||||
Palermo | Csa | 915 | 858 | ||||||||
Paris | Cfb | 2663 | 176 | ||||||||
Pescara | Cfa | 1793 | 497 | ||||||||
Plovdiv | ET | 2563 | 493 | ||||||||
Porto | Csb | 1526 | 211 | ||||||||
Prague | Dfb | 3549 | 135 | ||||||||
Rome | Csa | 1503 | 619 | ||||||||
Salamanca | Bsk | 2648 | 315 | ||||||||
[107] | Residential | Calama | BWk | 1990–2010 | N.A. | N.A. | RCP4.5 & RCP8.5 | Stochastic | 2045–2054 | H and C energy needs | 2019 |
Antofagasta | BWk | ||||||||||
Vallenar | BWk | ||||||||||
Valparaíso | Csb | ||||||||||
Santiago | Csb | ||||||||||
Concepción | Csb | ||||||||||
Temuco | Csb | ||||||||||
Punta Arenas | ET | ||||||||||
[61] | Hospital | Antananarivo | Cwb | 1961–1990 | 490 | 425 | Recorded data B1, A1B, A2 | Stochastic | 1990–2009 2030, 2060, 2090 | Yearly EC | 2019 |
Victoria | Af | 0 | 3223 | ||||||||
Moroni | Dfb | 0 | 2697 | ||||||||
Mamoudzou | Aw | N.A. | N.A. | ||||||||
Port-Louis | Aw | 0 | 1968 | ||||||||
Saint-Denis | As | 0 | 2510 | ||||||||
[108] | Residential | Greater Accra, Ghana | Aw | 2000–2009 | 0 | 3407 | A1B | Stochastic | 2030, 2050 | C EC | 2019 |
[109] | Residential | Izmir | Csa | N.A. | N.A. | N.A. | RCP8.5 | Morphing | 2060 | Yearly EC | 2019 |
Istanbul | Csa | ||||||||||
Ankara | Csb | ||||||||||
Erzurum | Dfb | ||||||||||
[110] | Residential | Santa Rosa | Cfa | 1961–1990 | 1580 | 619 | A2 | Morphing | 2080 | Yearly EC | 2019 |
Mendoza | BWk | 1386 | 909 | ||||||||
Cordoba | Csa | 1242 | 1013 | ||||||||
Oran | Cwa | 414 | 1550 | ||||||||
[59] | Restaurant, hospital, hotel, office, residential, school, retail, supermarket, warehouse | Los Angeles | Csb | 1991–2005 | 648 | 224 | A2 | Morphing | 2050 | ED | 2019 |
[18] | Campus | Ann Arbor | Dfa | 1970–1999 | N.A. | N.A. | RCP2.6, RCP4.5, RCP6.0, RCP8.5 | Morphing | 2010–2039 2040–2069 2070–2099 | Change in EC % | 2019 |
[69] | Residential | Valencia | Csa | 1961–1990 | 1167 | 765 | RCP4.5, RCP8.5 | Morphing | 2048–2052 2096–2100 | ED | 2018 |
[66] | Residential | Cordoba | Csa | 1971–2000 | 1121 | 936 | A2 | Morphing | 2050 | ED | 2018 |
[26] | Office | Marseille | Csa | 1979–2000 | 1735 | 578 | RCP2.6, RCP4.5, RCP6.0, RCP8.5 | Morphing | 2035, 2065, 2090 | ED | 2018 |
Montpellier | Csa | 1693 | 531 | ||||||||
Nice | Csa | 1454 | 551 | ||||||||
Athens | Csa | 1112 | 1076 | ||||||||
Thessaloniki | Cfa | 1741 | 792 | ||||||||
Genoa | Csa | 1348 | 653 | ||||||||
Messina | Csa | 758 | 1085 | ||||||||
Naples | Csa | 1364 | 756 | ||||||||
Palermo | Csa | 724 | 1022 | ||||||||
Pisa | Csa | 1757 | 520 | ||||||||
Rome | Csa | 1444 | 649 | ||||||||
Venice | Csa | 2262 | 526 | ||||||||
Barcelona | Csa | 1419 | 588 | ||||||||
Valencia | Csa | 1052 | 796 | ||||||||
Izmir | Csa | 1391 | 926 | ||||||||
[60] | Office | Harbin | Dwa, Cfa | 1961–2010 | 5229 | 362 | Recorded data | Recorded data | Yearly C loads (W/m2 per year) | 2018 | |
Tianjin | N.A. | N.A. | |||||||||
Shanghai | N.A. | N.A. | |||||||||
Guangzhou | N.A. | N.A. | |||||||||
[111] | Residential, hotel, office, school | Daytona | Cfa | 1961–1990 1991–2005 | 447 | 1576 | A2 | Morphing | 2020, 2050, 2080 | H and C demand | 2018 |
Jacksonville | Cfa | 1379 | 690 | ||||||||
Key West | Aw | 29 | 2790 | ||||||||
Miami | Am | 67 | 2442 | ||||||||
Orlando | Cfa | 282 | 1694 | ||||||||
Pensacola | Cfa | 624 | 1517 | ||||||||
Tallahassee | Cfa | 816 | 1309 | ||||||||
Tampa | Cfa | 375 | 1805 | ||||||||
[112] | Residential | Helsinki-Vantaa | Dfb | 1980–2009 | 4589 | 83 | B1, A1B, A2 | Morphing | 2030, 2050, 2100 | Net ED | 2018 |
[113] | Residential | Florence | Csa | 2000–2009 | 1767 | 906 | RCP8.5 | Morphing | 2036–2065 2066–2095 | H and C net energy needs | 2018 |
[114] | Commercial | Montreal | Dfb | 1953–1995 | 4493 | 234 | A2 | Morphing | 2020, 2050 | Yearly EC | 2018 |
[115] | Residential | Curitiba | Cfb | 1961–1990 | 886 | 305 | A2 | Morphing | 2020, 2050, 2080 | ED | 2016 |
Florianópolis | Cfa | 250 | 1077 | ||||||||
Belem | Af | 0 | 2896 | ||||||||
[63] | Office Residential | Philadelphia | Cfa | 1961–1990 | 2787 | 602 | A1F1, A2 | Morphing | 2040–2069 | H and C EU | 2017 |
Chicago | Dfa | 3557 | 431 | ||||||||
Phoenix | Bwh | 628 | 2280 | ||||||||
Miami | Am | 64 | 2369 | ||||||||
[89] | Residential | Santa Rosa | Cfa | 2011–2014 | N.A. | N.A. | RCP4.5 | Others | 2015–2039 | EC of gas and electricity | 2017 |
[62] | Residential | London | Cfb | 2011 | N.A. | N.A. | A2 | Morphing | 2020, 2050, 2080 | Yearly EC | 2017 |
[116] | Residential | Hong Kong, | Cwa | 1983–2005 | 202 | 2064 | Recorded data RCP4.5, RCP8.5 | Other | 2006–2014 2015–2044 | Yearly ED | 2017 |
Seoul | Dwa | 2782 | 560 | ||||||||
Tokyo | Cfa | 2311 | 508 | ||||||||
[117] | Office | Seoul | Dwa | 1961–1990 | 2925 | 658 | A2 | Morphing | 2020, 2050, 2080 | C EC | 2017 |
Tokyo | Cfa | 1730 | 846 | ||||||||
Hong Kong | Cwa | 215 | 2004 | ||||||||
[118] | Residential | Kaunas | Dfb | 1980–1999 | 4137 | 71 | RCP2.6, RCP8.5 | Morphing | 2020, 2050, 2080 | Primary EC | 2017 |
[58] | Residential, restaurant, hospital, hotel, office, outpatient, school, retail, mall, supermarket, warehouse | Different locations in US | Different climate zones | 1991–2005 | N.A. | N.A. | A1B, A2, B1 | Offset method | 2040, 2090 | Change in EC % | 2016 |
[119] | Office | Sapporo | Dfb | 1981–2000 | 3578 | 236 | A2 | Dynamical | 2040, 2090 | Energy loads | 2016 |
Tokyo | Cfa | 2311 | 508 | ||||||||
Naha | Cfa | 226 | 1969 | ||||||||
[90] | Residential | Tokyo | Cfa | 2005 | N.A. | N.A. | RCP4.5 | Dynamical | 2029 | Heat loads in August | 2016 |
[120] | Residential | Taipei | Cfa | 1993–2014 | N.A. | N.A. | A2, B2, A1B | Morphing | 2020, 2050, 2080 | Yearly C EC | 2016 |
[64] | Residential | Vaxjo | Cfb | 1961–1990 | 4174 | 38 | Recorded data RCP4.5, RCP8.5 | Morphing | 1996–2005 2050, 2090 | H/C demand | 2016 |
[121] | Residential | Qatar | BWh | 1961–1990 | 101 | 3253 | A2 | Morphing | 2080 | Yearly primary EU | 2016 |
[122] | School | Milan | Cfa | 1951–1970 | 1767 | 906 | A2 | Morphing | 2020, 2050, 2080 | H and C energy needs | 2016 |
[91] | Residential | Tokyo | Cfa | 2006–2010 | 1492 | 1029 | RCP4.5 | Dynamical | 2031–2035 | Heat loads in August | 2015 |
[123] | Residential, office, warehouse commercial | Florida | Cfa | 2004 | N.A. | N.A. | A2 | Statistical | 2052 2089 | Change in EC % | 2015 |
Louisiana | Cfa | ||||||||||
Minnesota | Dfb | ||||||||||
Missouri | Dfa | ||||||||||
New York | Dfa | ||||||||||
Virginia | Cfa | ||||||||||
[71] | Day-care centre | Copenhagen | Cfb | 1975–1989 | 3563 | 29 | A1B | Hourly, monthly and annual offset method | 2021–2050 | Yearly H/C demand | 2015 |
[67] | Office | Sydney | Cfa | 1982–1999 | 687 | 634 | A2 | Morphing | 2020, 2050, 2080 | EC | 2014 |
Melbourne | Cfb | 1733 | 210 | ||||||||
Canberra | Cfb | 2120 | 195 | ||||||||
Adelaide | Csb | 1122 | 479 | ||||||||
Darwin | Aw | 0 | 3355 | ||||||||
[80] | Residential | Tianjin | Dwa | 1971–2010 | 2735 | 867 | B1 A1B | PCA | 2011–2050 2051–2100 | H/C loads | 2014 |
[72] | Office | Vienna | Cfb | 1961–1990 | 3156 | 201 | Recorded data A1B | Recorded data Dynamical | 1980–2009 2011–2040 2036–2065 | Yearly ED | 2014 |
[81] | Office | Tianjin | Dwa | 1961–1970 1971–2010 | 2735 | 867 | Recorded data B1 A1B | Recorded data PCA | 2001–2010 2051–2100 | Heating loads (%) | 2013 |
[124] | Residential | Singapore | Af | 1990 | 0 | 3454 | N.A. | Offset method | +0.5 °C, +1.3 °C, +2.4 °C | Cooling loads (%) | 2013 |
[125] | Office | Hong Kong | Cwa | 1961–1990 | 215 | 2004 | A1B B1 | Morphing | 2011–2030 2046–2065 2080–2099 | Change in EC (%) | 2013 |
[126] | Office | Ningbo | Cfa | 1990–2009 | N.A. | N.A. | A2 | Morphing | 2010–2039 2040–2069 2070–2099 | ED | 2012 |
[36] | Residential | Montreal | Dfb | 1961–1990 | 3578 | 254 | A2 | Morphing | 2011–2040 2041–2070 | Electricity consumption | 2012 |
[82] | Office | Harbin | Dwa, Cfa, Cwb, Cwa | 1971–2000 | N.A. | N.A. | B1, A1B | PCA | 2001–2100 2009–2100 | H and C EU | 2012 |
Beijing | N.A. | N.A. | |||||||||
Shanghai | N.A. | N.A. | |||||||||
Kunming | N.A. | N.A. | |||||||||
Hong Kong | 202 | 2064 | |||||||||
[127] | Office | Burkina Faso | BSh | 1977–2010 | N.A. | N.A. | A1, A2, B2, B1 (average) | N.A. | 2010–2029 2030–2049 2060–2079 | Yearly C loads | 2012 |
[128] | Office School | Crete | Cfa | 1961–1990 | 774 | 1026 | A1B, A2, B2 | Other | 2041–2050 2091–2100 | H and C EU (kWh/m2) | 2012 |
West Central Macedonia | Csa | 1801 | 915 | ||||||||
Cyclades | Csa | 778 | 820 | ||||||||
Eastern Central Greece | BSh | N.A. | N.A. | ||||||||
[129] | Office Residential | Hong Kong | Cwa | 1979–2003 | 202 | 2064 | B1, A1B | Morphing | 2011–2030 2046–2065 2080–2099 | A/C EC | 2011 |
[130] | Residential | Darwin | Aw | N.A. | 0 | 3355 | +6 | Offset method | N.A. | H and C loads | 2011 |
Brisbane | Cfa | 329 | 1061 | ||||||||
Alice Springs, | Bwh | 665 | 1816 | ||||||||
Mildura | Bsh | 1160 | 769 | ||||||||
Sydney | Cfa | 687 | 634 | ||||||||
Melbourne | Cfb | 1733 | 210 | ||||||||
Hobart | Cfb | 2073 | 52 | ||||||||
Cabramurra | Cfb | 3586 | 49 | ||||||||
[131] | Residential | Dhaka | Aw | 1961–1990 | 10 | 2853 | A2 | Morphing | 2020 2050 2080 | Cooling ED | 2011 |
[83,132] | Office | Hong Kong | Cwa | 1979–2008 | 202 | 2064 | B1 A1B | PCA | 2009–2100 | H and C loads & Yearly EU | 2011 |
[88] | Residential | Athens | Csa | 1983–1992 | N.A. | N.A. | Recorded data | Recorded data | 1993–2002 | Energy requirements | 2010 |
Thessaloniki | Cfa | ||||||||||
[133] | Residential | Alice Springs | Bwh | 1990 | 665 | 1816 | 550ppm | Morphing | 2050 | Energy requirements (MJ/m2) | 2010 |
Darwin | Aw | 0 | 3355 | ||||||||
Hobart | Cfb | 2073 | 52 | ||||||||
Melbourne | Cfb | 1733 | 210 | ||||||||
Sydney | Cfa | 687 | 634 | ||||||||
[76] | Residential | Ljubljana | Cfb | 1961–1990 | 3208 | 201 | +1 °C + 3 °C Recorded data | Offset method Recorded data | 2050, 2003 | EU | 2010 |
Portoroz | 1829 | 577 | |||||||||
[77] | Residential | Al-Ain | Bwh | 1961–1990 | 61 | 577 | +1.6 °C, +2.9 °C, +2.3 °C, +5.9 °C Recorded data | Offset method | 2050 2100 | H, C, Fans, Electricity | 2009 |
[134] | Office | London | Cfb | 2005 | N.A. | N.A. | Medium-high | Morphing | 2010–2040 | H and C EU | 2008 |
Cardiff | |||||||||||
Birmingham | |||||||||||
Manchester | |||||||||||
Edinburgh | |||||||||||
[135] | Residential Office | Zurich–Kloten | Dfb | 1961–1990 | 3643 | 85 | +0.7 °C, +1 °C, +4.4 °C | Offset method | 1984–2003 2050–2100 | Yearly ED | 2005 |
[136] | Residential | Algarve | Csa | 1961–1990 | 979 | 669 | gga2 | Stochastic | 2080–2100 | H and C loads | 2002 |
South Inland | Csa | 1475 | 796 | ||||||||
Lisbon | Csa | 1059 | 608 | ||||||||
Centre Littoral | Csb | 1297 | 271 | ||||||||
Centre Inland | Csb | 1735 | 667 | ||||||||
North Littoral | Cfb | 1632 | 317 | ||||||||
North Inland | Csb | 2546 | 426 |
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Methodological Phases | Ref. | Input Variable | Variation | |
---|---|---|---|---|
1. | Study context | [20] | Geographical context | Different locations characterized by different Heating Degree Days (HDDs) and Cooling Degree Days (CDDs) |
[21] | Building typology | Residential, Commercial, etc. | ||
[22] | Reference period | Different baseline periods depending on the recorded data availability (TMY2, TMY3, IWEC) | ||
2. | Future weather files prediction | [1,23,24] | Storyline/Representative Concentration Pathways (RCP) | Emissions Scenarios (SRES), Representative Concentration Pathways (RCPs), Shared Socioeconomic Pathways (SSPs) |
[25] | Global Circulation Model (GCMs) | Single or combined GCMs | ||
[26] | Downscaling technique | Statistical (imposed offset method—i.e., morphing—or stochastic weather method) Dynamical (using Regional Climate Models, RCMs). Hybrid | ||
[27] | Weather file type | Typical Meteorological Year (TMY), Extreme Cold Year (ECY), Extreme Warm Year (EWY) | ||
[28] | Study period | Near term, middle term, long term | ||
3. | Energy consumption prediction | [29] | Building model | Dynamical energy simulation model, regression model (degree-days method) |
Downscaling Method | Advantages | Disadvantages |
---|---|---|
Statistical: stochastic method |
| |
Statistical: imposed offset method |
| |
Dynamical |
|
Variable | Brief Description | |
---|---|---|
P1. | Building typology | Type of building in accordance with usage |
P2. | Location | Reference city/region. When a study was referred to a region, the most representative city was selected. |
P3. | Climate zone 1 | Climatic zones in accordance with the Köppen-Geiger climate classification system [49] |
P4. | Heating Degree Days 1 | Calculated based on reference period and location (T = 18 °C) |
P5. | Cooling Degree Days 1 | Calculated based on reference period and location (T = 18 °C) |
P6. | Reference period | Baseline weather file for simulation in current climate conditions |
P7. | Emission scenario | Emission scenario adopted for future climate projections |
P8. | Downscaling technique | Technique used for generating the future weather files |
P9. | Future time slices | Future weather file for simulation in future climate conditions |
P10. | CO2 concentration (ppm) 1 | Selected in accordance with the emission scenario and the future time slice |
P11. | Target | Outcome measured |
P12. | Heating consumption variation 1 | Percentage variation between heating consumption in the reference period and in the future weather scenario considered |
P13. | Cooling consumption variation 1 | Percentage variation between heating consumption in the reference period and in the future weather scenario considered |
P14. | Total consumption variation 1 | Percentage variation between heating consumption in the reference period and in the future weather scenario considered |
Input Variable | Variation | Frequency (%) | Number of Items | |
---|---|---|---|---|
P6 | Emission scenario | A2 | 22 | 841 |
RCP8.5 | 12 | 148 | ||
A1B | 12 | 156 | ||
RCP4.5 | 10 | 91 | ||
B1 | 9 | 89 | ||
No scenario (recorded data) | 8 | 56 | ||
n.g. | 7 | 53 | ||
Other scenarios | 19 | 233 | ||
P7 | Downscaling technique | Morphing | 45 | 1083 |
Offset method | 10 | 145 | ||
Dynamical | 7 | 57 | ||
Stochastic | 7 | 147 | ||
Hybrid | 2 | 10 | ||
No downscaling (recorded data) | 13 | 56 | ||
PCA | 5 | 34 | ||
Other methods | 9 | 141 | ||
n.g. | 1 | 3 | ||
P8 | Reference period | 1990 | 39 | 581 |
2000 | 20 | 580 | ||
2010 | 37 | 485 | ||
n.g. | 4 | 30 | ||
P9 | Future time slice | 2020 | 25 | 485 |
2050 | 34 | 659 | ||
2080 | 31 | 454 | ||
Recorded data | 9 | 56 | ||
n.g. | 1 | 22 |
Overall Data | 2020 | 2050 | 2080 | |
---|---|---|---|---|
Heating variation | Median | −12.6% | −23.3% | −47.5% |
Mean | −18.83% | −30.28% | −48.72% | |
Standard deviation | 0.176 | 0.218 | 0.272 | |
Cooling variation | Median | 28.8% | 61.5% | 60.9% |
Mean | 32.1% | 72.3% | 204.1% | |
Standard deviation | 0.366 | 1.060 | 11.096 | |
Total variation | Median | 2.6% | 0.3% | 12.0% |
Mean | 5.23% | 4.73% | 20.36% | |
Standard deviation | 0.345 | 0.478 | 0.659 |
Variable | HDDs | CDDs | RP | CO2 | ΔH | ΔC | ΔT |
---|---|---|---|---|---|---|---|
HDDs | 1 | −0.759 | 0.120 | −0.239 | 0.458 | 0.038 | −0.445 |
CDDs | −0.759 | 1 | −0.212 | 0.161 | −0.326 | −0.198 | 0.280 |
RP | 0.120 | −0.212 | 1 | −0.082 | 0.050 | −0.129 | −0.012 |
CO2 | −0.239 | 0.161 | −0.082 | 1 | −0.415 | 0.230 | 0.288 |
ΔH | 0.458 | −0.326 | 0.050 | −0.415 | 1 | −0.234 | −0.273 |
ΔC | 0.038 | −0.198 | −0.129 | 0.230 | −0.234 | 1 | 0.239 |
ΔT | −0.445 | 0.280 | −0.012 | 0.288 | −0.273 | 0.239 | 1 |
Variable | HDDs | CDDs | RP | CO2 | ΔH | ΔC | ΔT |
---|---|---|---|---|---|---|---|
HDDs | 1 | −0.766 | 0.237 | −0.216 | 0.497 | 0.208 | −0.588 |
CDDs | −0.766 | 1 | −0.089 | 0.034 | −0.408 | −0.177 | 0.485 |
RP | 0.237 | −0.089 | 1 | −0.140 | 0.074 | 0.039 | −0.002 |
CO2 | −0.216 | 0.034 | −0.140 | 1 | −0.422 | 0.190 | 0.180 |
ΔH | 0.497 | −0.408 | 0.074 | −0.422 | 1 | −0.329 | −0.337 |
ΔC | 0.208 | −0.177 | 0.039 | 0.190 | −0.329 | 1 | 0.181 |
ΔT | −0.588 | 0.485 | −0.002 | 0.180 | −0.337 | 0.181 | 1 |
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Campagna, L.M.; Fiorito, F. On the Impact of Climate Change on Building Energy Consumptions: A Meta-Analysis. Energies 2022, 15, 354. https://doi.org/10.3390/en15010354
Campagna LM, Fiorito F. On the Impact of Climate Change on Building Energy Consumptions: A Meta-Analysis. Energies. 2022; 15(1):354. https://doi.org/10.3390/en15010354
Chicago/Turabian StyleCampagna, Ludovica Maria, and Francesco Fiorito. 2022. "On the Impact of Climate Change on Building Energy Consumptions: A Meta-Analysis" Energies 15, no. 1: 354. https://doi.org/10.3390/en15010354
APA StyleCampagna, L. M., & Fiorito, F. (2022). On the Impact of Climate Change on Building Energy Consumptions: A Meta-Analysis. Energies, 15(1), 354. https://doi.org/10.3390/en15010354