A semantic multi-criteria approach to evaluate different types of energy generation technologies

Abstract Multi-Criteria Decision Aid methods are used to find the best option from a set of alternatives when multiple and conflicting criteria have to be optimized simultaneously. The evaluation of the suitability or risk of each alternative is usually performed by assigning a numerical value. However, sometimes the data required to measure a criterion may be found in the form of semantic values such as tags. This paper proposes a methodology to calculate the strength of an outranking relation for a pair of alternatives using semantic criteria following the principles of ELECTRE-III (i.e. by means of concordance and discordance indices). The preferences about semantic data are represented in an ontology by means of objective and subjective functions. The paper explains how this new methodology was applied to analyse different electricity generation technologies using environmental and economic criteria. Two scenarios are tested to show how semantic criteria may influence the final decision.

[1]  Irene P. Koronaki,et al.  Strategic planning in the electricity generation sector through the development of an integrated Delphi-based multi-criteria evaluation model , 2013 .

[2]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[3]  Amgad Elgowainy,et al.  Updated greenhouse gas and criteria air pollutant emission factors and their probability distribution functions for electricity generating units , 2012 .

[4]  René Bañares-Alcántara,et al.  Managing Information to Support the Decision Making Process , 2012, J. Inf. Knowl. Manag..

[5]  Antonio Moreno,et al.  Preference Representation with Ontologies , 2013 .

[6]  Eric W. Stein,et al.  A comprehensive multi-criteria model to rank electric energy production technologies , 2013 .

[7]  Naim Afgan,et al.  MULTI-CRITERIA ASSESSMENT OF NEW AND RENEWABLE ENERGY POWER PLANTS , 2002 .

[8]  Konstantinos Aravossis,et al.  Decision making in renewable energy investments: A review , 2016 .

[9]  Antonio Moreno,et al.  Construction of an Outranking Relation Based on Semantic Criteria with ELECTRE-III , 2016, IPMU.

[10]  Veljko M. Milutinovic,et al.  Concepts, Ontologies, and Knowledge Representation , 2013, SpringerBriefs in Computer Science.

[11]  Eyke Hllermeier,et al.  Preference Learning , 2010 .

[12]  S French,et al.  Multicriteria Methodology for Decision Aiding , 1996 .

[13]  Maarten Sierhuis,et al.  Hypermedia Support for Argumentation-Based Rationale , 2006 .

[14]  Milosz Kadzinski,et al.  Co-constructive development of a green chemistry-based model for the assessment of nanoparticles synthesis , 2018, Eur. J. Oper. Res..

[15]  Salvatore Greco,et al.  An Overview of ELECTRE Methods and their Recent Extensions , 2013 .

[16]  Garvin A. Heath,et al.  Life cycle water use for electricity generation: a review and harmonization of literature estimates , 2013 .

[17]  Petros A. Pilavachi,et al.  Sensitivity analysis of the evaluation of power plants impact on the living standard using the analytic hierarchy process , 2008 .

[18]  Kuntz Werner,et al.  Issues as Elements of Information Systems , 1970 .

[19]  F. Henry Abanda,et al.  PV-TONS: A photovoltaic technology ontology system for the design of PV-systems , 2013, Eng. Appl. Artif. Intell..

[20]  Michel Parent,et al.  A Multicriteria Decision Making Approach for Carsharing Stations Selection , 2007, J. Decis. Syst..

[21]  Agis M. Papadopoulos,et al.  Application of the multi-criteria analysis method Electre III for the optimisation of decentralised energy systems , 2008 .

[22]  Alessio Ishizaka,et al.  Multi-criteria Decision Analysis: Methods and Software , 2013 .

[23]  Jiangjiang Wang,et al.  Review on multi-criteria decision analysis aid in sustainable energy decision-making , 2009 .

[24]  Adisa Azapagic,et al.  Life cycle sustainability assessment of UK electricity scenarios to 2070 , 2014 .

[25]  Dragan Komljenovic,et al.  A multicriteria decision making approach for evaluating renewable power generation sources in Saudi Arabia , 2016 .

[26]  J. Bergh,et al.  Optimal diversity of renewable energy alternatives under multiple criteria: An application to the UK , 2016 .

[27]  S. Stagl Multicriteria evaluation and public participation: the case of UK energy policy , 2006 .

[28]  Heracles Polatidis,et al.  Renewable energy projects: structuring a multi-criteria group decision-making framework , 2003 .

[29]  Maria Madalena Teixeira de Araújo,et al.  Evaluating future scenarios for the power generation sector using a Multi-Criteria Decision Analysis (MCDA) tool: The Portuguese case , 2013 .

[30]  Sanghyun Hong,et al.  Evaluating options for the future energy mix of Japan after the Fukushima nuclear crisis , 2013 .

[31]  Luciano Basto Oliveira,et al.  Sustainable expansion of electricity sector: Sustainability indicators as an instrument to support decision making , 2010 .

[32]  René Bañares-Alcántara,et al.  A new integrated tool for complex decision making: Application to the UK energy sector , 2013, Decis. Support Syst..

[33]  Dilek Küçük,et al.  Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles , 2014, ArXiv.

[34]  E. Georgopoulou,et al.  A multicriteria decision aid approach for energy planning problems: The case of renewable energy option , 1997 .

[35]  Garvin A. Heath,et al.  Review of Operational Water Consumption and Withdrawal Factors for Electricity Generating Technologies , 2011 .

[36]  Kannan Govindan,et al.  ELECTRE: A comprehensive literature review on methodologies and applications , 2016, Eur. J. Oper. Res..

[37]  Moncho Gómez-Gesteira,et al.  A sensitivity study of the WRF model in wind simulation for an area of high wind energy , 2012, Environ. Model. Softw..

[38]  A. Erener,et al.  A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM) , 2016 .