District Heating Systems Performance Analyses. Heat Energy Tariff

Abstract The paper addresses an important element of the European energy sector: the evaluation of district heating (DH) system operations from the standpoint of increasing energy efficiency and increasing the use of renewable energy resources. This has been done by developing a new methodology for the evaluation of the heat tariff. The paper presents an algorithm of this methodology, which includes not only a data base and calculation equation systems, but also an integrated multi-criteria analysis module using MADM/MCDM (Multi-Attribute Decision Making / Multi-Criteria Decision Making) based on TOPSIS (Technique for Order Performance by Similarity to Ideal Solution). The results of the multi-criteria analysis are used to set the tariff benchmarks. The evaluation methodology has been tested for Latvian heat tariffs, and the obtained results show that only half of heating companies reach a benchmark value equal to 0.5 for the efficiency closeness to the ideal solution indicator. This means that the proposed evaluation methodology would not only allow companies to determine how they perform with regard to the proposed benchmark, but also to identify their need to restructure so that they may reach the level of a low-carbon business.

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APLEISTŲ PASTATŲ RACIONALAUS NAUDOJIMO MODELIAVIMAS TAIKANT GEOINFORMACINES SISTEMAS

Geodesy and Cartography ◽

10.3846/13921541.2005.9636678 ◽

2012 ◽

Vol 31 (4) ◽

pp. 129-133 ◽

Cited By ~ 1

Author(s):

Jurgita Antuchevičienė

Keyword(s):

Decision Making ◽

Ideal Solution ◽

Order Preference ◽

Multi Attribute Decision Making ◽

Topsis Technique

Priimant erdvinius sprendimus geografinių informacinių sistemų (GIS) galimybės taikomos apleistų pastatų racionalaus naudojimo problemoms aplinkos ir visuomenės darnos požiūriu spręsti. Pasiūlytas pradinių duomenų parengimo ir jų taikymo skaičiavimams daugiatiksliais sprendimų priėmimo (Multi-attribute Decision Making – MADM) metodais modelis. Pateikta apleistų pastatų duomenų bazės struktūra ir parengtas šios bazės pildymo duomenimis apie nenaudojamus Lietuvos kaimo statinius pavyzdys. Sudaryti darnią ūkio plėtrą šalyje nusakančių socialinių, ekonominių ir aplinkos rodiklių sluoksniai. Numatytos duomenų analizės galimybės. Pateiktas GIS ir daugiatikslio sprendimų priėmimo metodo TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) integravimo racionaliam apleistų pastatų Lietuvos kaimo vietovėse naudojimui modeliuoti pavyzdys.

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INTEGRATED APPROACH OF FUZZY MULTI-ATTRIBUTE DECISION MAKING AND DATA MINING FOR CUSTOMER SEGMENTATION

E+M Ekonomie a Management ◽

10.15240/tul/001/2021-4-011 ◽

2021 ◽

Vol 24 (4) ◽

pp. 174-188

Author(s):

Manidatta Ray ◽

Mamata Ray ◽

Kamalakanta Muduli ◽

Audrius Banaitis ◽

Anil Kumar

Keyword(s):

Data Mining ◽

Decision Making ◽

Fuzzy Ahp ◽

Integrated Approach ◽

Fuzzy Topsis ◽

Ideal Solution ◽

Fuzzy C Means ◽

Order Preference ◽

Multi Attribute Decision Making ◽

Topsis Technique

This research work focuses on integrating the multi attribute decision making with data mining in a fuzzy decision environment for customer relationship management. The main objective is to analyse the relation between multi attribute decision making and data mining considering a complex problem of ordering customers segments, which is based on four criteria of customer’s life time value, viz. length (L), recency (R), frequency (F) and monetary value (M). The proposed integrated approach involves fuzzy C-means (FCM) cluster analysis as data mining tool. The experiment conducted using MATLAB 12.0 for identifying eight clusters of customers. The two multi attribute decision making tools i.e., fuzzy AHP (Analytic Hierarchy Process) and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) are used for ranking these identified clusters. The applicability of the integrated decision making technique is also demonstrated in this paper considering the case of Indian retail sector. This research collected responses from nine experts from Indian retail industry regarding their perception of relative importance of four criteria of customer life value and evaluated weights of each criterion using fuzzy AHP. Transaction data of 18 months of the case retail store was analysed to segment 1,600 customers into eight clusters using fuzzy c-means clustering analysis technique. Finally, these eight clusters were ranked using fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The findings of this research could be helpful for firms in identifying the more valuable customers for them and allocate more resources to satisfy them. The findings will be also helpful in developing different loyalty program strategies for customers of different clusters.

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Dynamic Multi-Attribute Decision Making Model with the Area Closeness Degree

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.321-324.2557 ◽

2013 ◽

Vol 321-324 ◽

pp. 2557-2560

Author(s):

Xi Juan Lou

Keyword(s):

Decision Making ◽

Ideal Solution ◽

Raw Data ◽

New Information ◽

Multi Attribute Decision Making ◽

Decision Making Model

The aim of this paper is to explore dynamic multi-attribute decision making (DMADM) problems in which the decision making information of alternatives is collected at different stages. Firstly, the area closeness degree is applied in normalizing the raw data. Secondly, the weights of different stages are determined by according to the principle of new information priority. The technique for preference by similarity to ideal solution (TOPSIS) is improved to aggregate the information from different stages. Finally, the example is illustrated to demonstrate the practicality and effectiveness of the proposed methods.

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A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

Complexity ◽

10.1155/2020/9017239 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Jianjun Cheng ◽

Wenbo Zhang ◽

Haijuan Yang ◽

Xing Su ◽

Tao Ma ◽

...

Keyword(s):

Decision Making ◽

Community Structure ◽

Community Detection ◽

Centrality Measures ◽

Ideal Solution ◽

Centrality Measure ◽

Detection Algorithms ◽

Pros And Cons ◽

Topsis Technique

The centrality plays an important role in many community-detection algorithms, which depend on various kinds of centralities to identify seed vertices of communities first and then expand each of communities based on the seeds to get the resulting community structure. The traditional algorithms always use a single centrality measure to recognize seed vertices from the network, but each centrality measure has both pros and cons when being used in this circumstance; hence seed vertices identified using a single centrality measure might not be the best ones. In this paper, we propose a framework which integrates advantages of various centrality measures to identify the seed vertices from the network based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiattribute decision-making technology. We take each of the centrality measures involved as an attribute, rank vertices according to the scores which are calculated for them using TOPSIS, and then take vertices with top ranks as the seeds. To put this framework into practice, we concretize it in this paper by considering four centrality measures as attributes to identify the seed vertices of communities first, then expanding communities by iteratively inserting one unclassified vertex into the community to which its most similar neighbor belongs, and the similarity between them is the largest among all pairs of vertices. After that, we obtain the initial community structure. However, the amount of communities might be much more than they should be, and some communities might be too small to make sense. Therefore, we finally consider a postprocessing procedure to merge some initial communities into larger ones to acquire the resulting community structure. To test the effectiveness of the proposed framework and method, we have performed extensive experiments on both some synthetic networks and some real-world networks; the experimental results show that the proposed method can get better results, and the quality of the detected community structure is much higher than those of competitors.

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Satisfaction Degree Based Interactive Decision Making under Hesitant Fuzzy Environment with Incomplete Weights

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488514500275 ◽

2014 ◽

Vol 22 (04) ◽

pp. 553-572 ◽

Cited By ~ 41

Author(s):

Huchang Liao ◽

Zeshui Xu

Keyword(s):

Decision Making ◽

Hesitant Fuzzy Set ◽

Optimization Models ◽

Multi Criteria Decision Making ◽

Ideal Solution ◽

Fuzzy Information ◽

Satisfaction Degree ◽

Interactive Decision Making ◽

New Research ◽

Topsis Technique

Multi-criteria decision making with hesitant fuzzy information is a new research topic since the hesitant fuzzy set was firstly proposed. This paper investigates a multi-criteria decision making problem where the weight information is partially known. We firstly propose the hesitant fuzzy positive ideal solution and the hesitant fuzzy negative ideal solution. Motivated by the TOPSIS (Technique for Order Preference by Similarity to an ideal Solution) method, we definite the satisfaction degree of an alternative, based on which several optimization models are derived to determinate the weights. Subsequently, in order to make a more reasonable decision, we introduce an interactive method based on some optimization models for multi-criteria decision making problems with hesitant fuzzy information. Finally, a practical example on evaluating the service quality of airlines is provided to illustrate our models and method.

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District heating systems based on low-carbon energy technologies in Mediterranean areas

Energy ◽

10.1016/j.energy.2016.11.096 ◽

2017 ◽

Vol 120 ◽

pp. 397-416 ◽

Cited By ~ 18

Author(s):

Jesús Lizana ◽

Carlos Ortiz ◽

Víctor M. Soltero ◽

Ricardo Chacartegui

Keyword(s):

District Heating ◽

Low Carbon ◽

Heating Systems ◽

Energy Technologies ◽

Mediterranean Areas ◽

District Heating Systems ◽

Low Carbon Energy

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Multi-criteria Analysis of District Heating Systems in Baltic States

Energy Procedia ◽

10.1016/j.egypro.2014.12.102 ◽

2014 ◽

Vol 61 ◽

pp. 2172-2175 ◽

Cited By ~ 16

Author(s):

Jeļena Ziemele ◽

Ieva Pakere ◽

Normunds Talcis ◽

Dagnija Blumberga

Keyword(s):

District Heating ◽

Baltic States ◽

Heating Systems ◽

District Heating Systems ◽

Multi Criteria Analysis

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A Comparative Study on Decision Making Methods with Interval Data

Journal of Computational Engineering ◽

10.1155/2014/793074 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Aditya Chauhan ◽

Rahul Vaish

Keyword(s):

Decision Making ◽

Input Data ◽

Multiple Criteria Decision Making ◽

Interval Data ◽

Cross Entropy ◽

Ideal Solution ◽

Order Preference ◽

Topsis Technique ◽

Selection Of ◽

Novel Algorithm

Multiple Criteria Decision Making (MCDM) models are used to solve a number of decision making problems universally. Most of these methods require the use of integers as input data. However, there are problems which have indeterminate values or data intervals which need to be analysed. In order to solve problems with interval data, many methods have been reported. Through this study an attempt has been made to compare and analyse the popular decision making tools for interval data problems. Namely, I-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), DI-TOPSIS, cross entropy, and interval VIKOR (VlseKriterijumska Optimiza-cija I Kompromisno Resenje) have been compared and a novel algorithm has been proposed. The new algorithm makes use of basic TOPSIS technique to overcome the limitations of known methods. To compare the effectiveness of the various methods, an example problem has been used where selection of best material family for the capacitor application has to be made. It was observed that the proposed algorithm is able to overcome the known limitations of the previous techniques. Thus, it can be easily and efficiently applied to various decision making problems with interval data.

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Trends in the development of the district heating systems of Ukraine

The Problems of General Energy ◽

10.15407/pge2021.01.052 ◽

2021 ◽

Vol 2021 (1) ◽

pp. 52-59

Author(s):

V.O. Derii ◽

Keyword(s):

Thermal Energy ◽

Heat Supply ◽

District Heating ◽

Hot Water ◽

Low Carbon ◽

Heating Systems ◽

Fuel Balance ◽

Generation Structure ◽

District Heating Systems ◽

Low Carbon Development

We considered trends in the development of district heating systems (DHS) in Europe and Ukraine. It was established that DHS are widely used and make a significant contribution to the heat supply of European countries. In the European Union as a whole, the share of DHS is 13%, and there are plans to increase it to 50% in 2050 with a wide use of cogeneration and renewable sources of energy, including environmental energy with using heat pumps. Ukraine is one of the countries with a high level of DHS, but, at present, there are negative trends to reducing their contribution to the total heat supply for heating and hot water supply – from 65.2% in 2014 to 52% in 2017. In several cities, DHS ceased to function at all. The main equipment of the DHS of Ukraine is physically worn out and technologically obsolete and needs to be renewed by means of wide reconstruction, modernization, and technological re-equipment. We determined factors and the level of their influence on the demand in thermal energy of DHS. It was established that the factors reducing demand have a much greater potential. We created forecasts of demand for thermal energy, fuel balance, and the structure of DHS generation by 2050. It is shown that the demand for thermal energy from DHS will decrease and reach about 35 million Gcal in 2050. To ensure the low-carbon development of Ukraine in the structure of thermal energy generation in DHS, the use of coal-fired CHPs and boilers, as well as boilers on petroleum products will be significantly reduced. The share of natural gas in the fuel balance of DHS of Ukraine will also decrease, but it will be the main fuel for the period of technological transformation of generating capacities under conditions of the low-carbon development of Ukraine. The use of technologies for the production of thermal energy from biomass, waste, environment, and electricity will gradually increase, and in 2050, using these sources will produce about 23.8 million Gcal, which is more than 60% of the total thermal energy of DHS. Keywords: district heating systems, thermal energy, factors of influence, demand, fuel balance, generation structure

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Supportiveness of Low-Carbon Energy Technology Policy Using Fuzzy Multicriteria Decision-Making Methodologies

Mathematics ◽

10.3390/math8071178 ◽

2020 ◽

Vol 8 (7) ◽

pp. 1178 ◽

Cited By ~ 2

Author(s):

Konstantinos Kokkinos ◽

Vayos Karayannis

Keyword(s):

Decision Making ◽

Swot Analysis ◽

Technology Policy ◽

Multicriteria Decision Making ◽

Clean Energy ◽

Policy Formulation ◽

Ideal Solution ◽

Low Carbon ◽

Multicriteria Decision ◽

Low Carbon Energy

The deployment of low-carbon energy (LCE) technologies and management of installations represents an imperative to face climate change. LCE planning is an interminable process affected by a multitude of social, economic, environmental, and health factors. A major challenge for policy makers is to select a future clean energy strategy that maximizes sustainability. Thus, policy formulation and evaluation need to be addressed in an analytical manner including multidisciplinary knowledge emanating from diverse social stakeholders. In the current work, a comparative analysis of LCE planning is provided, evaluating different multicriteria decision-making (MCDM) methodologies. Initially, by applying strengths, weaknesses, opportunities, and threats (SWOT) analysis, the available energy alternative technologies are prioritized. A variety of stakeholders is surveyed for that reason. To deal with the ambiguity that occurred in their judgements, fuzzy goal programming (FGP) is used for the translation into fuzzy numbers. Then, the stochastic fuzzy analytic hierarchical process (SF-AHP) and fuzzy technique for order performance by similarity to ideal solution (F-TOPSIS) are applied to evaluate a repertoire of energy alternative forms including biofuel, solar, hydro, and wind power. The methodologies are estimated based on the same set of tangible and intangible criteria for the case study of Thessaly Region, Greece. The application of FGP ranked the four energy types in terms of feasibility and positioned solar-generated energy as first, with a membership function of 0.99. Among the criteria repertoire used by the stakeholders, the SF-AHP evaluated all the criteria categories separately and selected the most significant category representative. Finally, F-TOPSIS assessed these criteria ordering the energy forms, in terms of descending order of ideal solution, as follows: solar, biofuel, hydro, and wind.

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