IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i11p2059-d447037.html
   My bibliography  Save this article

Fuzzy Cognitive Maps Optimization for Decision Making and Prediction

Author

Listed:
  • Katarzyna Poczeta

    (Department of Information Systems, Kielce University of Technology, 25-314 Kielce, Poland)

  • Elpiniki I. Papageorgiou

    (Department of Energy Systems, Faculty of Technology, University of Thessaly, Geopolis Campus, GR 41500 Larissa, Greece)

  • Vassilis C. Gerogiannis

    (Department of Digital Systems, Faculty of Technology, University of Thessaly, Geopolis Campus, GR 41500 Larissa, Greece)

Abstract

Representing and analyzing the complexity of models constructed by data is a difficult and challenging task, hence the need for new, more effective techniques emerges, despite the numerous methodologies recently proposed in this field. In the present paper, the main idea is to systematically create a nested structure, based on a fuzzy cognitive map (FCM), in which each element/concept at a higher map level is decomposed into another FCM that provides a more detailed and precise representation of complex time series data. This nested structure is then optimized by applying evolutionary learning algorithms. Through the application of a dynamic optimization process, the whole nested structure based on FCMs is restructured in order to derive important relationships between map concepts at every nesting level as well as to determine the weights of these relationships on the basis of the available time series. This process allows discovering and describing hidden relationships among important map concepts. The paper proposes the application of the suggested nested approach for time series forecasting as well as for decision-making tasks regarding appliances’ energy consumption prediction.

Suggested Citation

  • Katarzyna Poczeta & Elpiniki I. Papageorgiou & Vassilis C. Gerogiannis, 2020. "Fuzzy Cognitive Maps Optimization for Decision Making and Prediction," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2059-:d:447037
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/11/2059/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/8/11/2059/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Konstantinos Papageorgiou & Gustavo Carvalho & Elpiniki I. Papageorgiou & Dionysis Bochtis & George Stamoulis, 2020. "Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique," Energies, MDPI, vol. 13(6), pages 1-23, March.
    2. Jin-Young Kim & Sung-Bae Cho, 2019. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder," Energies, MDPI, vol. 12(4), pages 1-14, February.
    3. Spyros Makridakis & Robert L. Winkler, 1983. "Averages of Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 29(9), pages 987-996, September.
    4. Karin Kandananond, 2011. "Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach," Energies, MDPI, vol. 4(8), pages 1-12, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Konstantinos Kokkinos & Eftihia Nathanail, 2023. "A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO 2 Urban Mobility: The Case of Larissa, Greece," Sustainability, MDPI, vol. 15(16), pages 1-30, August.
    2. Orang, Omid & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha, 2023. "Multi-output time series forecasting with randomized multivariate Fuzzy Cognitive Maps," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Luz E. Gutiérrez & José Javier Samper & Daladier Jabba & Wilson Nieto & Carlos A. Guerrero & Mark M. Betts & Héctor A. López-Ospina, 2023. "Combined Framework of Multicriteria Methods to Identify Quality Attributes in Augmented Reality Applications," Mathematics, MDPI, vol. 11(13), pages 1-39, June.
    4. Katarzyna Poczeta & Elpiniki I. Papageorgiou, 2022. "Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks," Energies, MDPI, vol. 15(20), pages 1-18, October.
    5. Shruthi Dakey & Sameer Deshkar & Shreya Joshi & Vibhas Sukhwani, 2023. "Enhancing Resilience in Coastal Regions from a Socio-Ecological Perspective: A Case Study of Andhra Pradesh, India," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    6. Mohammad Javad Bidel & Hossein Safari & Hannan Amoozad Mahdiraji & Edmundas Kazimieras Zavadskas & Jurgita Antucheviciene, 2022. "A Framework for Project Delivery Systems via Hybrid Fuzzy Risk Analysis: Application and Extension in ICT," Mathematics, MDPI, vol. 10(17), pages 1-22, September.
    7. Viktorija Terjanika & Jelena Pubule, 2022. "Barriers and Driving Factors for Sustainable Development of CO 2 Valorisation," Sustainability, MDPI, vol. 14(9), pages 1-16, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bilgili, Faik, 2002. "VAR, ARIMA, Üstsel Düzleme, Karma ve İlave-Faktör Yöntemlerinin Özel Tüketim Harcamalarına ait Ex Post Öngörü Başarılarının Karşılaştırılması [A Comparison of Ex-Post Forecast Accuracies for VAR, A," MPRA Paper 75536, University Library of Munich, Germany, revised 2002.
    2. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
    3. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    4. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    5. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    6. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    7. Srinivas Bollapragada & Salil Gupta & Brett Hurwitz & Paul Miles & Rajesh Tyagi, 2008. "NBC-Universal Uses a Novel Qualitative Forecasting Technique to Predict Advertising Demand," Interfaces, INFORMS, vol. 38(2), pages 103-111, April.
    8. Chen, Jiandong & Xu, Chong & Shahbaz, Muhammad & Song, Malin, 2021. "Interaction determinants and projections of China’s energy consumption: 1997–2030," Applied Energy, Elsevier, vol. 283(C).
    9. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    10. Zhenni Ding & Huayou Chen & Ligang Zhou, 2023. "Using shapely values to define subgroups of forecasts for combining," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 905-923, July.
    11. Maria A. Franco & Stefan N. Groesser, 2021. "A Systematic Literature Review of the Solar Photovoltaic Value Chain for a Circular Economy," Sustainability, MDPI, vol. 13(17), pages 1-35, August.
    12. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    13. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    14. Samuels, Jon D. & Sekkel, Rodrigo M., 2017. "Model Confidence Sets and forecast combination," International Journal of Forecasting, Elsevier, vol. 33(1), pages 48-60.
    15. Julia A. Minson & Jennifer S. Mueller & Richard P. Larrick, 2018. "The Contingent Wisdom of Dyads: When Discussion Enhances vs. Undermines the Accuracy of Collaborative Judgments," Management Science, INFORMS, vol. 64(9), pages 4177-4192, September.
    16. Robert L. Winkler & Robert T. Clemen, 2004. "Multiple Experts vs. Multiple Methods: Combining Correlation Assessments," Decision Analysis, INFORMS, vol. 1(3), pages 167-176, September.
    17. Becerra-Fernandez, Mauricio & Sarmiento, Alfonso T. & Cardenas, Laura M., 2023. "Sustainability assessment of the solar energy supply chain in Colombia," Energy, Elsevier, vol. 282(C).
    18. Park, Timothy A. & Gubanova, Tatiana & Lohr, Luanne & Escalante, Cesar L., 2005. "Forecasting Organic Food Prices: Testing and Evaluating Conditional Predictive Ability," 2005 Annual meeting, July 24-27, Providence, RI 19412, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    19. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2021. "Distributional regression for demand forecasting in e-grocery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 831-842.
    20. Hensher, D. A. & Louviere, J. J. & Hansen, D. E., 2000. "The use of mixtures of market and experimental choice data in establishing guideline weights for evaluating competitive bids in a transport organisation," Transport Policy, Elsevier, vol. 7(4), pages 279-286, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2059-:d:447037. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.