2022
DOI: 10.3390/en15072411
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A Comparison of Multi-Criteria Decision Analysis Methods for Sustainability Assessment of District Heating Systems

Abstract: The sustainability of energy systems is increasingly assessed for development of more resilient, greener district heating (DH) systems. That requires compiling technological, environmental, and economic indicators in a social, political, and institutional context. This work investigates DH system sustainability analysis by five frequently applied multi-criteria decision analysis (MCDA) methods—WSM, TOPSIS, PROMETHEE, ELECTRE and DEA. To compare the sustainability assessment results, a selection of 8 criteria d… Show more

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Cited by 20 publications
(11 citation statements)
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“…This disadvantage is mitigated in this study by discussing results on aggregated levels as well as on the detailed criteria level. In (Terrapon-Pfaff, 2014) and (Daugavietis et al, 2022) the authors compare results obtained with different methods, including SAW, and conclude that results using the SAW method do not differ significantly from the results obtained with more complex methods. Furthermore, the SAW method is suitable to be applied with fuzzy numbers for uncertain input data such as subjective expert evaluations.…”
Section: Validity Of the Resultsmentioning
confidence: 99%
“…This disadvantage is mitigated in this study by discussing results on aggregated levels as well as on the detailed criteria level. In (Terrapon-Pfaff, 2014) and (Daugavietis et al, 2022) the authors compare results obtained with different methods, including SAW, and conclude that results using the SAW method do not differ significantly from the results obtained with more complex methods. Furthermore, the SAW method is suitable to be applied with fuzzy numbers for uncertain input data such as subjective expert evaluations.…”
Section: Validity Of the Resultsmentioning
confidence: 99%
“…The MADM [57,58] has been applied to evaluate and rank different machine learning algorithms based on their performance across various criteria (attributes). In this case, the decision matrix consists of rows representing different machine learning algorithms and columns representing different performance metrics (attributes) such as accuracy, FPR (false positive rate), precision, F1-score, TPR (true positive rate), and TNR (true negative rate).…”
Section: Results Analysismentioning
confidence: 99%
“…Changes in the sets of alternatives and criteria over time [32] Selection of a wine supplier for a supermarket by a group of decision-makers Although static MCDM methods are widely used in energy and energy management problems [42,43], only a few studies listed in Table 1 relate TMCDM and DMCDM methods to energy problems. These are the problems of selecting an investment portfolio based on renewable energy [40], assessing the energy systems of European countries [41] and assessing the energy security of OECD countries [18].…”
Section: Mcdm Methodsmentioning
confidence: 99%