Analyzing energy poverty with Fuzzy Cognitive Maps: A step-forward towards a more holistic approach

References

• Amer, M., A. Jetter, and T. Daim. 2011. Development of fuzzy cognitive map (FCM)-based scenarios for wind energy. International Journal of Energy Sector Management 5 (4):564–84. doi:10.1108/17506221111186378.
   Google Scholar
• Amirkhani, A., E. I. Papageorgiou, A. Mohseni, and M. R. Mosavi. 2017. A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications. In Computer Methods and Programs in Biomedicine, Elsevier Ireland Ltd. doi:10.1016/j.cmpb.2017.02.021.
   Google Scholar
• Antosiewicz, M., A. Nikas, A. Szpor, J. Witajewski-Baltvilks, and H. Doukas, 2019. Pathways for the transition of the Polish power sector and associated risks. Environmental Innovation and Societal Transitions, In Press, Corrected Proof. 10.1016/j.eist.2019.01.008
   Google Scholar
• Axelrod, R., 1976. Structure of decisions: The cognitive maps of political elites. The Structure of Decision the Cognitive Maps of Political Elite (p. 422). 10.1017/S0008423900042797
   Google Scholar
• Bank, W., 2016, Weathering the Crisis: Reducing the Gaps in Social Protection in Greece. Accessed August 12, 2018. https://www.dianeosis.org/wp-content/uploads/2017/11/world_bank.pdf, .
   Google Scholar
• Bertoldi, P., F. Diluiso, L. Castellazzi, N. Labanca, and T. Ribeiro Serrenho, 2018. Energy Consumption and Energy Efficiency Trends in the EU-28 2000-2015, EUR 29104 EN, Publications Office of the European Union, Luxembourg, doi:10.2760/6684
   Google Scholar
• Boardman, B., 1986. Seasonal mortality and cold homes. Unhealthy Housing: A Diagnosis conference. Proceedings of the University of Warwick’s.
   Google Scholar
• Bollino, C. A., and F. Botti. 2017. Energy poverty in Europe: A multidimensional approach. PSL Quarterly Review 70 (283):473–507.
   Web of Science ®Google Scholar
• Borah, P., M. K. Singh, and S. Mahapatra. 2015. Estimation of degree-days for different climatic zones of North-East India. Sustainable Cities and Society 14:70–81. doi:10.1016/j.scs.2014.08.001.
   Web of Science ®Google Scholar
• Bourgani, E., C. D. Stylios, G. Manis, and V. C. Georgopoulos, 2013. Fuzzy cognitive maps modeling and simulation. In: Proceedings of 25th European Modeling and Simulation Symposium, EMSS 2013, Athens, Greece, 561–70. 10.3748/wjg.v19.i4.561
   Google Scholar
• Bouzarovski, S. 2014. Energy poverty in the European Union: Landscapes of vulnerability. Wiley Interdisciplinary Reviews: Energy and Environment 3 (3):276–89. doi:10.1002/wene.89.
   Web of Science ®Google Scholar
• Bouzarovski, S., 2018. ENERGY POVERTY. (Dis)Assembling Europe’s Infrastructural Divide, Palgrave Macmillan, Cham. DOI: 10.1007/978-3-319-69299-9
   Google Scholar
• Bouzarovski, S., and S. Petrova. 2015. A global perspective on domestic energy deprivation: Overcoming the energy poverty–Fuel poverty binary. Energy Research & Social Science 10:31–40. doi:10.1016/j.erss.2015.06.007.
   Web of Science ®Google Scholar
• Bouzarovski, S., S. Petrova, and R. Sarlamanov. 2012. Energy poverty policies in the EU: A critical perspective. Energy Policy 49:76–82. doi:10.1016/j.enpol.2012.01.033.
   Web of Science ®Google Scholar
• Bouzarovski-Buzar, S. 2011. Energy poverty in the EU: A review of the evidence. Brussels: DG Regio workshop on Cohesion policy investing in energy efficiency in buildings.
   Google Scholar
• BPIE (Buildings Performance Institute Europe), 2014. Alleviating fuel poverty in the EU. Investing in home renovation, a sustainable and inclusive solution. BPIE, Brussels.
   Google Scholar
• Bradshaw, J., and S. Hutton. 1983. Social policy options and fuel poverty. Journal Of Economic Psychology 3:249–66. doi:10.1016/0167-4870(83)90005-3.
   Web of Science ®Google Scholar
• Brown, S. P., and M. K. Yücel. 2008. What drives natural gas prices?. The Energy Journal 29 (2):45–60.
   Web of Science ®Google Scholar
• Bueno, S., and J. L. Salmeron. 2009. Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications 36 (3 PART 1):5221–29. doi:10.1016/j.eswa.2008.06.072.
   Web of Science ®Google Scholar
• Buzar, S. 2007a. Energy Poverty in Eastern Europe: Hidden Geographies of Deprivation. Aldershot, UK: Ashgate.
   Google Scholar
• Buzar, S. 2007b. When homes become prisons: The relational spaces of post-socialist energy poverty. Environment and Planning A 39:1908–25. doi:10.1068/a38298.
   Web of Science ®Google Scholar
• Chester, L., and A. Morris. 2011. A new form of energy poverty is the hallmark of liberalised electricity sectors. Australian Journal of Social Issues 46 (4):435–59. doi:10.1002/(ISSN)1839-4655.
   Web of Science ®Google Scholar
• Coady, D., and A. Dizioli. 2018. Income inequality and education revisited: Persistence, endogeneity and heterogeneity. Applied Economics 50 (25):2747–61. doi:10.1080/00036846.2017.1406659.
   Web of Science ®Google Scholar
• D’Agostino, D., and D. Parker. 2018. Data on cost-optimal nearly zero energy buildings (NZEBs) across Europe. Data in Brief 17:1168–74. doi:10.1016/j.dib.2018.02.038.
   PubMed Web of Science ®Google Scholar
• DECC, 2015. Annual Fuel Poverty Statistics Report 2015. Department for Energy and Climate Change, DECC, London.
   Google Scholar
• Eden, C., F. Ackermann, and S. Cropper. 1992. The analysis of cause maps. Journal of Management Studies 29 (3):309–24. doi:10.1111/j.1467-6486.1992.tb00667.x.
   Web of Science ®Google Scholar
• EESC (European Economic and Social Committee), 2013. Opinion TEN 516: For coordinated European measures to prevent and combat energy poverty (Rapporteurs: Coulon and H. Bataller). Brussels.
   Google Scholar
• EIA (U.S. Energy Information Administration), 2018. Annual Energy Outlook 2018 with projections to 2050. Office of Energy Analysis, U.S. Department of Energy. Washington, DC 20585
   Google Scholar
• EPEE (European Fuel Poverty and Energy Efficiency), 2009. Diagnosis of causes and consequences of fuel poverty in Belgium, France, Italy, Spain and United Kingdom. WP2-Deliverable 5. Dixit Productions, Boulogne, France.
   Google Scholar
• Eurostat, 2018a. EU statistics on income and living conditions (EU-SILC) methodology - Economic strain. Accessed July, 2018. http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=ilc_mdes01&lang=en
   Google Scholar
• Eurostat, 2018b. EU statistics on income and living conditions (EU-SILC) methodology - Economic strain. Accessed July, 2018 http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=ilc_mdes07&lang=en
   Google Scholar
• Eurostat, 2018c. Energy consumption in households. Accessed July, 2018 http://ec.europa.eu/eurostat/statistics-explained/index.php/Energy_consumption_in_households
   Google Scholar
• Felix, G., G. Nápoles, R. Falcon, W. Froelich, K. Vanhoof, and R. Bello. 2017. A review on methods and software for fuzzy cognitive maps. Artificial Intelligence Review 1–31. doi:10.1007/s10462-017-9575-1.
   Google Scholar
• Filčák, R., and L. Živčič. 2017. Energy poverty and multi-dimensional perspectives of social inequalities and policy challenges. International Issues & Slovak Foreign Policy Affairs 26(1–2): 40–61.
   Google Scholar
• Foster, D., 2016. Why is one older person dying every seven minutes during the winter? Accessed August 10, 2018. https://www.theguardian.com/society/2016/jan/20/older-person-dying-winter-fuel-poverty
   Google Scholar
• Gan, J. 2007. Supply of biomass, bioenergy, and carbon mitigation: Method and application. Energy Policy 35 (12):6003–09. doi:10.1016/j.enpol.2007.08.014.
   Web of Science ®Google Scholar
• Gan, J., and C. T. Smith. 2006. A comparative analysis of woody biomass and coal for electricity generation under various CO2 emission reductions and taxes. Biomass and Bioenergy 30 (4):296–303. doi:10.1016/j.biombioe.2005.07.006.
   Web of Science ®Google Scholar
• Giabbanelli, P. J., and R. Crutzen. 2014. Creating groups with similar expected behavioural response in randomized controlled trials: A fuzzy cognitive map approach. BMC Medical Research Methodology 14 (1):130. doi:10.1186/1471-2288-14-130.
   PubMedGoogle Scholar
• Giabbanelli, P. J., S. A. Gray, and P. Aminpour. 2017. Combining fuzzy cognitive maps with agent-based modeling: Frameworks and pitfalls of a powerful hybrid modeling approach to understand human-environment interactions. Environmental Modelling and Software 95:320–25. doi:10.1016/j.envsoft.2017.06.040.
   Web of Science ®Google Scholar
• Giabbanelli, P. J., T. Torsney-Weir, and V. K. Mago. 2012. A fuzzy cognitive map of the psychosocial determinants of obesity. Applied Soft Computing Journal 12 (12):3711–24. doi:10.1016/j.asoc.2012.02.006.
   Web of Science ®Google Scholar
• Gray, S., J. Hilsberg, A. McFall, and R. Arlinghaus. 2015a. The structure and function of angler mental models about fish population ecology: The influence of specialization and target species. Journal of Outdoor Recreation and Tourism 12:1–13. doi:10.1016/j.jort.2015.09.001.
   Google Scholar
• Gray, S. A., S. Gray, J. L. de Kok, A. E. R. Helfgott, B. O’Dwyer, R. Jordan, and A. Nyaki. 2015b. Using fuzzy cognitive mapping as a participatory approach to analyze change, preferred states, and perceived resilience of social-ecological systems. Ecology and Society 20 (2). doi:10.5751/ES-07396-200211.
   Web of Science ®Google Scholar
• Gray, S. R. J., A. S. Gagnon, S. A. Gray, B. O’Dwyer, C. O’Mahony, D. Muir, R. J. N. Devoy, M. Falaleeva, and J. Gault. 2014. Are coastal managers detecting the problem? Assessing stakeholder perception of climate vulnerability using Fuzzy Cognitive Mapping. Ocean and Coastal Management 94:74–89. doi:10.1016/j.ocecoaman.2013.11.008.
   Web of Science ®Google Scholar
• Greek Government Gazette, 2017. Adoption and application of the Technical Directives of the Technical Chamber of Greece about the Energy Performance of Buildings. Issue B, No. 4003, 17/ 11/2017,48597 – 49196. [in Greek]
   Google Scholar
• Grevisse, F., and M. Brynart, 2011. Energy Poverty in Europe: Towards a more global understanding. ECEEE 2011 Summer Study. Energy Efficiency First: The Foundation of a Low-carbon Society.
   Google Scholar
• Groumpos, P. 2017. Why Model Complex Dynamic Systems Using Fuzzy Cognitive Maps?. Robotics and Automation Engineering Journal 1 (3):555563. doi:10.19080/RAEJ.2017.01.555563.
   Google Scholar
• Hage, P., and F. Harary. 1983. Structural models in anthropology. New York, NY: Oxford University Press.
   Google Scholar
• Hammoudeh, S., H. Li, and B. Jeon. 2003. Causality and volatility spillovers among petroleum prices of WTI, gasoline and heating oil in different locations. The North American Journal of Economics and Finance 14 (1):89–114. doi:10.1016/S1062-9408(02)00112-2.
   Google Scholar
• Healy, J. D., and J. P. Clinch, 2002. Fuel poverty in Europe: Across-country analysis using a new composite measurement. Environmental Studies Research Series. Working Papers. University College Dublin. ESRS02/04. Accessed May 2015 http://www.ucd.ie/gpep/research/archivedworkingpapers/2002/02-04.pdf.
   Google Scholar
• Hellenic Statistical Authority. 2012. Household energy consumption survey, 2011-2012. Piraeus: Hellenic Statistical Authority.
   Google Scholar
• Hellenic Statistical Authority, 2013a. Survey on household energy consumption, 2011-2012. Press release. Hellenic Statistical Authority, October 2013, Accessed August 10, 2018. http://www.statistics.gr/documents/20181/e74d6134-8c02-404e-a02b-aa6d959219e3.
   Google Scholar
• Hellenic Statistical Authority, 2013b. Press Release: Energy consumption survey in households, 2011-2012. Piraeus, Greece. (In Greek)
   Google Scholar
• Hsu, H.-M., and C.-T. Chen. 1996. Aggregation of fuzzy opinions under group decision making. Fuzzy Sets and Systems 79 (3):279–85. doi:10.1016/0165-0114(95)00185-9.
   Web of Science ®Google Scholar
• IEA (International Energy Agency), 2011. Evaluating the co-benefits of low-incomes energy-efficiency programmes. Results of the Dublin Workshop, 27-28 January 2011. IEA Publications, Paris, France.
   Google Scholar
• Jabłońska, M., S. Viljainen, J. Partanen, and T. Kauranne. 2012. The impact of emissions trading on electricity spot market price behavior. International Journal of Energy Sector Management 6 (3):343–64. doi:10.1108/17506221211259664.
   Google Scholar
• Jetter, A. J., and K. Kok. 2014. Fuzzy Cognitive Maps for futures studies-A methodological assessment of concepts and methods. Futures 61:45–57. doi:10.1016/j.futures.2014.05.002.
   Web of Science ®Google Scholar
• Jones, S., A. Tod, H. Thomson, M. De Groote, F. Anagnostopoulos, S. Bouzarovski, S. Tirado Herrero, C. Snell, A. Dobbins, S. Pye, et al. 2016. Energy Poverty Handbook. Brussels: The Greens/EFA group in the European Parliament.
   Google Scholar
• Jurado, F. 2003. Fuzzy Logic Control of a Combined-Cycle Power Plant Using Biomass. Energy Sources 25 (2):113–21. doi:10.1080/00908310390142172.
   Web of Science ®Google Scholar
• Katsoulakos, N. 2011. Combating energy poverty in mountainous areas through energy-saving interventions: Insights from Metsovo, Greece. Mountain Research and Development 31 (4):284–92. doi:10.1659/MRD-JOURNAL-D-11-00049.1.
   Web of Science ®Google Scholar
• Katsoulakos, N., 2013. Optimal use of renewable energy sources in mountainous areas. The case of Metsovo, Greece. Doctoral thesis, National Technical University of Athens, School of Mining and Metallurgical Engineering, Athens. [in Greek]
   Google Scholar
• Katsoulakos, N. M., and D. C. Kaliampakos. 2014. What is the impact of altitude on energy demand? A step towards developing specialized energy policy for mountainous areas. Energy Policy 71:130–38. doi:10.1016/j.enpol.2014.04.003.
   Web of Science ®Google Scholar
• Katsoulakos, N. M., and D. C. Kaliampakos. 2016. Mountainous areas and decentralized energy planning: Insights from Greece. Energy Policy 91:174–88. doi:10.1016/j.enpol.2016.01.007.
   Web of Science ®Google Scholar
• Konti, A., and D. Damigos. 2018. Exploring strengths and weaknesses of bioethanol production from bio-waste in Greece using Fuzzy Cognitive Maps. Energy Policy 112:4–11. doi:10.1016/j.enpol.2017.09.053.
   Web of Science ®Google Scholar
• Kontogianni, A., C. Tourkolias, and E. I. Papageorgiou. 2013. Revealing market adaptation to a low carbon transport economy: Tales of hydrogen futures as perceived by fuzzy cognitive mapping. International Journal of Hydrogen Energy 38 (2):709–22. doi:10.1016/j.ijhydene.2012.10.101.
   Web of Science ®Google Scholar
• Kosko, B. 1986. Fuzzy cognitive maps. International Journal of Man-Machine Studies 24 (1):65–75. doi:10.1016/S0020-7373(86)80040-2.
   Google Scholar
• Kosko, B. 1988. Hidden patterns in combined and adaptive knowledge networks. International Journal of Approximate Reasoning 2 (4):377–93. doi:10.1016/0888-613X(88)90111-9.
   Google Scholar
• Kosko, B. 1992. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Krajhanzl, J., 2010. Environmental and proenvironmental behavior. School and Health 21, Health Education: International Experiences. Accessed July, 2018. http://www.ped.muni.cz/z21/knihy/2011/36/36/texty/eng/krajhanzl.pdf
   Google Scholar
• Legendre, B., and O. Ricci. 2015. Measuring fuel poverty in France: Which households are the most fuel vulnerable? Energy Economics 49:620–28. doi:10.1016/j.eneco.2015.01.022.
   Web of Science ®Google Scholar
• Lewis, P. 1982. Fuel poverty can be stopped. Bradford: National Right to Fuel Campaign.
   Google Scholar
• MacDonald, N. 1983. Trees and networks in biological models. New York, NY: John Wiley and Sons.
   Google Scholar
• Mago, V. K., H. K. Morden, C. Fritz, T. Wu, S. Namazi, P. Geranmayeh, R. Chattopadhyay, and V. Dabbaghian. 2013. Analyzing the impact of social factors on homelessness: A Fuzzy Cognitive Map approach. BMC Medical Informatics and Decision Making 13 (1):94. doi:10.1186/1472-6947-13-94.
   PubMedGoogle Scholar
• Malek, Ž. 2016. Fuzzy-logic cognitive mapping: Introduction and overview of the method. In Environmental Modeling with Stakeholders: Theory, Methods, and Applications, ed. S. Gray, M. Paolisso, R. Jordan, and S. Gray, 127–43. Cham, Switzerland: Springer International Publishing.
   Google Scholar
• Matzarakis, A., and C. Balafoutis. 2004. Heating degree days over Greece as an index of energy consumption. International Journal Climatology 24:1817–28. doi:10.1002/(ISSN)1097-0088.
   Web of Science ®Google Scholar
• Maxim, A., C. Mihai, C. M. Apostoaie, C. Popescu, C. Istrate, and I. Bostan. 2016. Implications and measurement of energy poverty across the European Union. Sustainability 8 (5):483. doi:10.3390/su8050483.
   Web of Science ®Google Scholar
• Middlemiss, L., and R. Gillard. 2015. Fuel poverty from the bottom-up: Characterising household energy vulnerability through the lived experience of the fuel poor. Energy Research & Social Science 6:146–54. doi:10.1016/j.erss.2015.02.001.
   Web of Science ®Google Scholar
• Misthos, L.-M., G. Messaris, D. Damigos, and M. Menegaki. 2017. Exploring the perceived intrusion of mining into the landscape using the fuzzy cognitive mapping approach. Ecological Engineering 101:60–74. doi:10.1016/j.ecoleng.2017.01.015.
   Web of Science ®Google Scholar
• Mpartzas, K., and C. Nikolaidis, 2009. Estimation of heating and cooling degree-days at a 4h period and several base temperatures for 20 Greek cities. Bsc dissertation. Technological Educational Institute of Serres, Department of Mechanical Engineering, Serres. [in Greek]
   Google Scholar
• Nápoles, G., M. Leon Espinosa, I. Grau, K. Vanhoof, and R. Bello. 2018b. Fuzzy cognitive maps based models for pattern classification: Advances and challenges. In Studies in Fuzziness and Soft Computing (Vol 360:83–98.
   Google Scholar
• Nápoles, G., M. L. Espinosa, I. Grau, and K. Vanhoof. 2018a. FCM Expert: Software Tool for Scenario Analysis and Pattern Classification Based on Fuzzy Cognitive Maps. International Journal on Artificial Intelligence Tools 27 (07):1860010. doi:10.1142/S0218213018600102.
   Google Scholar
• Neocleous, C. C., and C. N. Schizas, 2004. Application of fuzzy cognitive maps to the political-economic problem of Cyprus. Proceedings of the International Conference on Fuzzy Sets and Soft Computing in Economics and Finance, Saint-Petersburg, Russia, 340–349.
   Google Scholar
• Nikas, A., E. Ntanos, and H. Doukas. 2019. A semi-quantitative modelling application for assessing energy efficiency strategies. Applied Soft Computing Journal 76:140–55. doi:10.1016/j.asoc.2018.12.015.
   Web of Science ®Google Scholar
• Nikas, A., V. Stavrakas, A. Arsenopoulos, H. Doukas, M. Antosiewicz, J. Witajewski-Baltvilks, and A. Flamos. 2018. Barriers to and consequences of a solar-based energy transition in Greece. Environmental Innovation and Societal Transitions, in Press, Corrected Proof. doi:10.1016/j.eist.2018.12.004.
   Google Scholar
• Özesmi, U., and S. L. Özesmi. 2004. Ecological models based on people’s knowledge: A multi-step fuzzy cognitive mapping approach. Ecological Modelling 176 (1–2):43–64. doi:10.1016/j.ecolmodel.2003.10.027.
   Web of Science ®Google Scholar
• Palmer, G., T. MacInnes, and P. Kenway. 2008. Cold and Poor: An Analysis of the Link Between Fuel Poverty and Low Income. London: New Policy Institute.
   Google Scholar
• Papada, L., 2017. Development of stochastic model for energy poverty analysis in Greece. The case of mountainous areas (Doctoral thesis). National Technical University of Athens, School of Mining and Metallurgical Engineering, Athens (in Greek).
   Google Scholar
• Papada, L., and D. Kaliampakos. 2016a. Measuring energy poverty in Greece. Energy Policy 94:157–65. doi:10.1016/j.enpol.2016.04.004.
   Web of Science ®Google Scholar
• Papada, L., and D. Kaliampakos. 2016b. Developing the energy profile of mountainous areas. Energy 107:205–14. doi:10.1016/j.energy.2016.04.011.
   Web of Science ®Google Scholar
• Papada, L., and D. Kaliampakos. 2017. Energy poverty in Greek mountainous areas: A comparative study. Journal of Mountain Science 14 (6):1229–40. doi:10.1007/s11629-016-4095-z.
   Web of Science ®Google Scholar
• Papada, L., and D. Kaliampakos. 2018. A Stochastic Model for energy poverty analysis. Energy Policy 116:153–64. doi:10.1016/j.enpol.2018.02.004.
   Web of Science ®Google Scholar
• Papada, L., and D. Kaliampakos. 2019. Development of vulnerability index for energy poverty. Energy & Buildings 183:761–71. doi:10.1016/j.enbuild.2018.11.033.
   Web of Science ®Google Scholar
• Papageorgiou, E. I., C. D. Stylios, and P. P. Groumpos. 2003. Fuzzy cognitive map learning based on nonlinear Hebbian rule. In Lecture notes in artificial intelligence, 2903, ed. T. D. Gedeon and L. C. C. Fung, 254–66. Berlin Heidelberg New York: Springer.
   Google Scholar
• Papageorgiou, E. I. 2011. A new methodology for Decisions in Medical Informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Applied Soft Computing 11 (1):500–13. doi:10.1016/j.asoc.2009.12.010.
   Web of Science ®Google Scholar
• Papageorgiou, E. I. 2012. Learning Algorithms for Fuzzy Cognitive Maps—A Review Study. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42 (2):150–63. doi:10.1109/TSMCC.2011.2138694.
   PubMed Web of Science ®Google Scholar
• Papageorgiou, E. I., and A. Kontogianni. 2012. Using Fuzzy Cognitive Mapping in Environmental Decision Making and Management: A Methodological Primer and an Application. International Perspectives on Global Environmental Change, ed. S. Y. Stephen and S. R. Silvern, 427–50. London, UK: InTech. Available at http://www.intechopen.com/books/international-perspectives-on-global-environmental-change/using-fuzzy-cognitive-mapping-in-environmental-decision-making-and-management-a-methodological-prime
   Google Scholar
• Papageorgiou, E. I. 2013. Review study on Fuzzy Cognitive Maps and their applications during the last decade. In Business Process Management, ed. M. Glykas, 281–298. Berlin Heidelberg: Springer.
   Google Scholar
• Papageorgiou, E. I., C. Stylios, and P. P. Groumpos. 2006. Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links. International Journal of Human Computer Studies 64 (8):727–43. doi:10.1016/j.ijhcs.2006.02.009.
   Web of Science ®Google Scholar
• Papageorgiou, E. I., C. D. Stylios, and P. P. Groumpos. 2004. Active Hebbian learning algorithm to train fuzzy cognitive maps. International Journal of Approximate Reasoning 37 (3):219–49. doi:10.1016/j.ijar.2004.01.001.
   Web of Science ®Google Scholar
• Papageorgiou, E. I., and J. L. Salmeron. 2013. A review of fuzzy cognitive maps research during the last decade. IEEE Transactions on Fuzzy Systems 21 (1):66–79. doi:10.1109/TFUZZ.2012.2201727.
   Web of Science ®Google Scholar
• Papageorgiou, E. I., N. I. Papandrianos, G. Karagianni, G. C. Kyriazopoulos, and D. Sfyras. 2009. A fuzzy cognitive map based tool for prediction of infectious diseases. Fuzzy Systems, FUZZ-IEEE 2009:2094–99.
   Google Scholar
• Papageorgiou, E. I., and P. P. Groumpos. 2005. A weight adaptation method for fuzzy cognitive map learning. Soft Computing 9 (11):846–57. doi:10.1007/s00500-004-0426-z.
   Web of Science ®Google Scholar
• Papakostas, G. A., D. E. Koulouriotis, A. S. Polydoros, and V. D. Tourassis. 2012. Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems. Expert Systems with Applications 39 (12):10620–29. doi:10.1016/j.eswa.2012.02.148.
   Web of Science ®Google Scholar
• Paz-Ortiz, I., and C. Gay-García. 2015. Fuzzy Cognitive Mapping and Nonlinear Hebbian Learning for the Qualitative Simulation of the Climate System, from a Planetary Boundaries Perspective. In Simulation and Modeling Methodologies, Technologies and Applications. Advances in Intelligent Systems and Computing, ed. M. Obaidat, T. Ören, J. Kacprzyk, and J. Filipe, Vol. 402, 295–312. Cham: Springer.
   Google Scholar
• Pillutla, V. S., and P. J. Giabbanelli. 2019. Iterative generation of insight from text collections through mutually reinforcing visualizations and fuzzy cognitive maps. Applied Soft Computing 76:459–72. doi:10.1016/j.asoc.2018.12.020.
   Web of Science ®Google Scholar
• Pye, S., A. Dobbins, C. Baffert, J. Brajković, I. Grgurev, R. De Miglio, and P. Deane, 2015. Energy poverty and vulnerable consumers in the energy sector across the EU: analysis of policies and measures. Policy Report. INSIGHT_E.
   Google Scholar
• Roberts, D., E. Vera-Toscano, and E. Phimister. 2015. Fuel poverty in the UK: Is there a difference between rural and urban areas? Energy Policy 87:216–23. doi:10.1016/j.enpol.2015.08.034.
   Web of Science ®Google Scholar
• Robinson, C., S. Bouzarovski, and S. Lindley. 2018a. Getting the measure of fuel poverty: The geography of fuel poverty indicators in England. Energy Research & Social Science 36:79–93. doi:10.1016/j.erss.2017.09.035.
   Web of Science ®Google Scholar
• Robinson, C., S. Bouzarovski, and S. Lindley. 2018b. Underrepresenting neighbourhood vulnerabilities? The measurement of fuel poverty in England. Environment and Planning A: Economy and Space50 (5):1109–27.
   Web of Science ®Google Scholar
• Ruksans, O., I. Oleinikova, and R. Prohorova, 2014. Analysis of factors that are affecting electricity prices in Baltic countries. Power and Electrical Engineering of Riga Technical University (RTUCON), 2014 55th International Scientific Conference,Riga, Latvia, 232–37. IEEE.
   Google Scholar
• Salmeron, J. L. 2010. Modelling grey uncertainty with fuzzy grey cognitive maps. Expert Systems with Applications 37 (12):7581–88. doi:10.1016/j.eswa.2010.04.085.
   Web of Science ®Google Scholar
• Santamouris, M., J. A. Paravantis, D. Founda, D. Kolokotsa, P. Michalakakou, A. M. Papadopoulos, N. Kontoulis, A. Tzavali, E. K. Stigka, Z. Ioannidis, et al. 2013. Financial crisis and energy consumption: a household survey in Greece. Energy and Buildings 65:477–87. doi:10.1016/j.enbuild.2013.06.024.
   Web of Science ®Google Scholar
• Santamouris, M., K. Kapsis, D. Korres, I. Livada, C. Pavlou, and M. N. Assimakopoulos. 2007. On the relation between the energy and social characteristics of the residential sector. Energy and Buildings 39:893–905. doi:10.1016/j.enbuild.2006.11.001.
   Web of Science ®Google Scholar
• Scarpellini, S., P. Rivera-Torres, I. Suárez-Perales, and A. Aranda-Usón. 2015. Analysis of energy poverty intensity from the perspective of the regional administration: Empirical evidence from households in southern Europe. Energy Policy 86:729–38. doi:10.1016/j.enpol.2015.08.009.
   Web of Science ®Google Scholar
• Sedki, K., and L. B. De Beaufort, 2012. Cognitive maps for knowledge represenation and reasoning. Proceedings – International Conference on Tools with Artificial Intelligence, ICTAI, Athens, Greece, Vol. 1, pp. 1035–40. doi:10.1109/ICTAI.2012.175.
   Google Scholar
• Sendich, E. 2014. The importance of natural gas in the industrial sector with a focus on energy-intensive industries. EIA Discussion Paper, Washington, DC.
   Google Scholar
• Sovacool, B. K. 2012. The political economy of energy poverty: A review of key challenges. Energy for Sustainable Development 16 (3):272–82. doi:10.1016/j.esd.2012.05.006.
   Web of Science ®Google Scholar
• Spyridaki, N.-A., S. Banaka, and A. Flamos. 2016. Evaluating public policy instruments in the Greek building sector. Energy Policy 88:528–43. doi:10.1016/j.enpol.2015.11.005.
   Web of Science ®Google Scholar
• Stylios, C. D., and P. P. Groumpos, 1999. Mathematical formulation of fuzzy cognitive maps. Proceedings of the 7th Mediterranean Conference on Control and Automation, Haifa, Israel, 2251–61.
   Google Scholar
• Stylios, C. D., and P. P. Groumpos. 2004. Modeling Complex Systems Using Fuzzy Cognitive Maps. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 34 (1):155–62. doi:10.1109/TSMCA.2003.818878.
   Web of Science ®Google Scholar
• Taylor, M., 2012. Growing number of pensioners face fuel poverty, Accessed August 10, 2018. https://www.theguardian.com/society/2012/jan/23/growing-numbers-pensioners-fuel-poverty.
   Google Scholar
• Thomson, H., and C. Snell. 2013. Quantifying the prevalence of fuel poverty across the European Union. Energy Policy 52:563–72. doi:10.1016/j.enpol.2012.10.009.
   Web of Science ®Google Scholar
• Thomson, H., S. Bouzarovski, and C. Snell. 2017. Rethinking the measurement of energy poverty in Europe: A critical analysis of indicators and data. Indoor and Built Environment 26 (7):879–901. doi:10.1177/1420326X17699260.
   PubMed Web of Science ®Google Scholar
• Tutmez, Β., and Α. Dag. 2007. Use of Fuzzy Logic in Lignite Inventory Estimation. Energy Sources, Part B: Economics, Planning, and Policy 2 (1):93–103.
   Web of Science ®Google Scholar
• van Vliet, M., K. Kok, and T. Veldkamp. 2010. Linking stakeholders and modellers in scenario studies: The use of Fuzzy Cognitive Maps as a communication and learning tool. Futures 42 (1):1–14. doi:10.1016/j.futures.2009.08.005.
   Web of Science ®Google Scholar
• Vasslides, J. M., and O. P. Jensen. 2016. Fuzzy cognitive mapping in support of integrated ecosystem assessments: Developing a shared conceptual model among stakeholders. Journal of Environmental Management 166:348–56. doi:10.1016/j.jenvman.2015.10.038.
   PubMed Web of Science ®Google Scholar
• Welsch, H., and P. Biermann, 2014. Energy Prices, Energy Poverty, and Well-Being: Evidence for European Countries (Vol. 369, No. 14). Oldenburg Discussion Papers in Economics.
   Google Scholar
• WHO (World Health Organization), 2016. Household air pollution and health. Fact sheet N°292. Accessed May, 2017. http://www.who.int/mediacentre/factsheets/fs292/en/
   Google Scholar
• Wilcox, J. A. 2007. Forecasting components of consumption with components of consumer sentiment. Business Economics 42 (4):22–32. doi:10.2145/20070403.
   Google Scholar
• Wrapson, W., and P. Devine-Wright. 2014. ‘Domesticating’ low carbon thermal technologies: Diversity, multiplicity and variability in older person, off grid households. Energy Policy 67:807–17. doi:10.1016/j.enpol.2013.11.078.
   Web of Science ®Google Scholar
• Yohanis, Y. G. 2012. Domestic energy use and householders’ energy behaviour. Energy Policy 41:654–65. doi:10.1016/j.enpol.2011.11.028.
   Web of Science ®Google Scholar
• YPEKA, 2011. Application Guide of the Programme “Energy Saving at Home”. Ministry of Environment, Energy and Climate Change, Athens. [in Greek]
   Google Scholar
• YPEKA, 2018. Application Guide of the Programme “Energy Saving at Home II”. Ministry of Environment, Energy and Climate Change, Athens. [in Greek]
   Google Scholar
• Zhang, P., and A. Jetter, 2017. Understanding risk perception using Fuzzy Cognitive Maps. PICMET 2016 – Portland International Conference on Management of Engineering and Technology: Technology Management For Social Innovation, Proceedings, Honolulu, HI. doi:10.1109/PICMET.2016.7806749
   Google Scholar