An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas
Abstract
:1. Introduction
Background
2. Materials and Methods
2.1. Study Area
2.2. Methodology
- Step 1—defining the analysis goal. This is identifying the energy efficiency potential in urban areas, building energy scenarios depending on the decision-maker, and analyzing the acceptable solution set M:
- Step 2—defining of the initial set of criteria and analysis taking into account the representativeness of the criteria, interrelationships between the criteria, and the level of detail of the description of the subject of assessment; construction of the final set of criteria C with the number n:
- Step 3—setting the criteria weights with the participation of the decision-maker and experts: because the criteria have unequal validity, hierarchical factors (weights) should be entered in the analysis process:
- Step 4—determination of numerical measures of individual analysis variants:
- Step 5—assessment of variant solutions based on a synthetic assessment. The result is finding the most advantageous variant in terms of the adopted criteria—the ability to make appropriate decisions. To do this, the value of each variant should be calculated using one of the synthetic formulas, which in the case of research is the adjusted summation index, defined as:
- Phase 1—clarifying the purpose of the analysis—determining the energy potential in individual decision variants and defining the initial set of criteria, and, as a consequence, determining the final criteria set affecting energy consumption in buildings. At the same time, preparation of GIS layers using ArcGIS 10.7—vectorization of reference units and determination of the prevailing category of buildings.
- Phase 2—determining the weighting of criteria with the participation of decision-makers and experts, determining the numerical measures of individual variants subject to analysis, and coding partial measures of individual variants using standardization. Calculations were carried out using MATLABTM software. Because in this study the criteria and weightings of the criteria were developed based on the opinions of decision-makers and experts, preliminarily prepared reports were forwarded to experts for assessment, improvement, and possible supplementation. All experts had knowledge and experience in the research topic.
- Phase3—analysis of the results obtained using GIS tools, which allowed verification of the results achieved depending on the decision variant, expert opinions, and the predominant category of objects located in reference units.
3. Results and Discussion
- -
- Construction time in quarters (Cr1)— five periods picked out that represented buildings in the quarters: until 1965, 1966–1985, 1986–1992, 1993–2008, and from 2009. The time intervals were determined based on the dominant period of ongoing construction investments.
- -
- Overwhelming function (Cr2)—four overwhelming functions have been distinguished: residential, residential-service, service, and industrial.
- -
- Predominant manufacturing technology (Cr3)—two dominant ways of constructing objects (technology): traditional and prefabricated.
- -
- The predominant source of heat (Cr4)—three types of urban areas have been distinguished: with a predominance of buildings heated with a solid fuel boiler, with a predominance of buildings supplied with heat by CHP, with a predominance of buildings heated with cold gas boilers.
- -
- The possibility of using energy from renewable energy sources (Cr5)—three levels showing the willingness of decision-makers to invest in renewable energy sources and taking into account the technical possibilities of obtaining energy from renewable energy sources: low, medium, high.
- -
- Cost of bringing 1 m2 of the facility to EU requirements for energy efficiency (Cr6)—the cost has been estimated for individual categories of buildings (A, B, C, D) based on the data contained in the study [44].
3.1. Discussion
3.2. Summary of Research
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Input Data | Criteria for Buildings Energy Consumption | Building Groups Categories | Decisions Variants |
construction years (in quarters) | category A—multi-family buildings made in prefabricated and traditional technology | Option 1—equal validity of all criteria | |
overwhelming feature | category B—single-family buildings made in traditional technology | Option 2—the decision-maker is the owner of the building or apartment | |
prevailing state of ownership | category C—buildings with a service function made in traditional technology | Option 3—the local government is the decision-maker | |
prevailing construction technology | category D—large-area industrial and service buildings constructed in prefabricated technology | Option 4—the energy company is the decision-maker | |
predominant source of heat | Option 5—decision-makers are representatives of organizations related to environmental protection | ||
possibility of using energy from RES | |||
cost of bringing 1 m2 of the facility to EU requirements for energy efficiency |
Criterion | Buildings Category | |||
---|---|---|---|---|
A | B | C | D | |
creation time | 0.1743 | 0.000 | 0.0000 | −0.1743 |
function | 0.1714 | 0.0343 | −0.0343 | −0.1714 |
ownership status | −0.1214 | 0.1486 | −0.1229 | 0.0943 |
construction technology | 0.1714 | 0.0343 | −0.0343 | −0.1714 |
heat source | 0.1243 | 0.1243 | −0.1243 | −0.1243 |
use of energy from RES | 0.0500 | −0.2100 | 0.0514 | 0.1086 |
coast of adapting 1 m2 surface to UE requirements | −0.0714 | 0.2143 | −0.0714 | -0.0714 |
Total Adjusted Indicator | 0.4986 | 0.3457 | −0.3357 | −0.5100 |
Total Adjusted Indicator | Buildings Category | |||
---|---|---|---|---|
A | B | C | D | |
Variant 1 | 0.4986 | 0.3457 | −0.3357 | −0.5100 |
Variant 2 | 0.3964 | 0.3588 | −0.3936 | −0.3896 |
Variant 3 | 0.6355 | 0.5015 | −0.3890 | −0.7490 |
Variant 4 | 0.1651 | 0.5266 | −0.3618 | −0.3304 |
Variant 5 | 0.3926 | −0.3021 | −0.0875 | −0.0033 |
The Sum of m2 Usable Floor Area | Energy Potential [PLN]/[€] | ||||
---|---|---|---|---|---|
Variant 1 | Variant 2 | Variant 3 | Variant 4 | Variant 5 | |
4,233,104 | 31,909,323/ | 26,996,982/ | 42,587,563/ | 21,119,910/ | 9,621,691/ |
7,252,119 | 6,135,678 | 9,678,991 | 4,799,979 | 2,186,748 |
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Sztubecka, M.; Skiba, M.; Mrówczyńska, M.; Bazan-Krzywoszańska, A. An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas. Remote Sens. 2020, 12, 259. https://doi.org/10.3390/rs12020259
Sztubecka M, Skiba M, Mrówczyńska M, Bazan-Krzywoszańska A. An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas. Remote Sensing. 2020; 12(2):259. https://doi.org/10.3390/rs12020259
Chicago/Turabian StyleSztubecka, Małgorzata, Marta Skiba, Maria Mrówczyńska, and Anna Bazan-Krzywoszańska. 2020. "An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas" Remote Sensing 12, no. 2: 259. https://doi.org/10.3390/rs12020259
APA StyleSztubecka, M., Skiba, M., Mrówczyńska, M., & Bazan-Krzywoszańska, A. (2020). An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas. Remote Sensing, 12(2), 259. https://doi.org/10.3390/rs12020259