Skip to main content
SpringerLink
Log in

Efficiency in Energy Decision Support Systems Using Soft Computing Techniques

  • Chapter
  • First Online:
Intelligent Decision Support Systems for Sustainable Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 705))

Abstract

In the recent years, the advent of globalization has caused a steep rise in energy consumption especially for the most developed countries. At the same time, there is an important need for energy planning especially from the energy distributors’ point of view as, it is vital for setting energy wholesale prices and profit margins. The same argument also holds for the case of energy transition from fossil fuels to more environmentally friendly forms since, it is necessary to predict future energy demands in order to achieve sustainability by balancing energy supply and demand. Even though a lot of research has been produced towards the development of energy models that try to optimize sustainability, we claim that, there is a need for multi-criteria Decision Support Systems, (DSS) that integrate a variety of econometric and computational intelligence methodologies in order to evaluate the impacts of a mix of sometimes conflicting factors such as climatological conditions, global prices, availability etc. In this paper, we propose an integrated DSS that involves Adaptive Neuro Fuzzy Systems (ANFIS ), Neural Networks, (NN) and Fuzzy Cognitive Maps, (FCM) along with Econometric Models, (EM) in a hybrid fashion to predict energy consumption and prices. Such a system defines the basic structure in building energy networks that use combined heat and power. Even though the system can be applied for the totality of energy sources, we focus only on the prediction methodologies for the natural gas consumption for the country level. We provide a comparative analysis of the aforementioned methods by obtaining the MSA, RMSE, MAE and MAPE errors, as well as R2 metric.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Energy Technology Perspectives (International Energy Agency Publications, 2016). ISBN PRINT 978-92-64-25234-9

    Google Scholar 

  2. SorinNeascu, Silvian Suditu and CătălinPopescu, An optimized way for a better accuracy of gas consumption profiles, Recent Researches in Electric Power and Energy Systems, in Proceedings of the 13th International Conference on Electric Power Systems, High Voltages, Electric Machines and Energy Systems (POES ‘13), (Chania, Crete, Greece, 2013) August 27–29

    Google Scholar 

  3. B. Soldo, Forecasting natural gas consumption. Appl. Energy 92, 26–37 (2012)

    Article  Google Scholar 

  4. A. Khotanzad, H. Elragal, Natural gas load forecasting with combination of adaptive neural networks, in Proceedings of the International Joint Conference on Neural Networks, 1999, pp. 4069–4072

    Google Scholar 

  5. A. Khotanzad, H. Elragal, T.L. Lu, Combination of artificial neural-network fore-casters for prediction of natural gas consumption. IEEE Trans. Neural Netw. 11(2), 464–4730 (2000)

    Article  Google Scholar 

  6. R.H. Brown, P. Kharouf, F. Xin, L.P. Piessens, D. Nestor, Development of feed-forward network models to predict gas consumption, Neural Networks, in IEEE World Congress on Computational Intelligence, 1994, pp. 802–805

    Google Scholar 

  7. R.H. Brown, I. Matin, Development of artificial neural network models to predict daily gas consumption, in Industrial Electronics, Control, and Instrumentation, Proceedings of the 1995 IEEE IECON, pp. 1389–1394

    Google Scholar 

  8. H.K. Ozturk, O.E. Canyurt, A. Hepbasli, Z. Utlu, Residential-commercial energy input estimation based on genetic algorithm (GA) approaches: an application of Turkey. Energy Build. 36(2), 175–183 (2004)

    Article  Google Scholar 

  9. D. Ivezic, Short-term natural gas consumption forecast. FME Trans. 34, 165–169 (2006)

    Google Scholar 

  10. B.B. Ekici, U.T. Aksoy, Prediction of building energy consumption by using artificial neural networks. Adv. Eng. Softw. 40, 356–362 2009

    Google Scholar 

  11. A. Azadeh, S.M. Asadzadeh, M. Saberi, V. Nadimi, A. Tajvidi, A neuro-fuzzy stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: the cases of Bahrain, Saudi Arabia, Syria, and UAE. Appl. Energy 40, 1432–1445 (2011)

    Google Scholar 

  12. A. Azadeh, S.M. Asadzadeh, A. Ghanbari, An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: uncertain and complex environments. Energy Policy 38(3), 1529–1536 (2010)

    Article  Google Scholar 

  13. A. Behrouznia, M. Saberi, A. Azadeh, S.M. Asadzadeh, P. Pazhoheshfar, An adaptive network based fuzzy inference system-fuzzy data envelopment analysis for gas consumption forecasting and analysis: the case of South America, in International conference on intelligent and advanced systems, (2010) Article number 5716160

    Google Scholar 

  14. V. MajaziDalfard, M. NazariAsli, S.M. Asadzadeh, S.M. Sajjadi, A. Nazari-Shirkouhi, A mathematical modeling for incorporating energy price hikes into total natural gas consumption forecasting. Appl. Math. Model. 37(8), 5664–5679 (2013)

    Article  Google Scholar 

  15. J. Wang, H. Jiang, Q. Zhou, J. Wu, S. Qin, China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model. Renew. Sustain. Energy Rev. 53, 1149–1167 (2016)

    Article  Google Scholar 

  16. E. Arsenault, J.T. Bernard, C.W. Carr, E. Genest-Laplante, A total energy demand model of Québec. Forecast. Prop. Energy Econ. 17, 163–171 (1995)

    Article  Google Scholar 

  17. I.D. McAvinchey, A. Yannopoulos, Stationarity, structural change and specification in a demand system: the case of energy. Energy Econ. 25, 65–92 (2003)

    Article  Google Scholar 

  18. F. Gori, C. Takanen, Forecast of energy consumption of industry and household and services in Italy. Heat Technol. 22, 115–121 (2004)

    Google Scholar 

  19. C.H. Skiadas, L.L. Papayannakis, A.G. Mourelatos, An attempt to improve the forecasting ability of growth functions: The Greek electric system. Technol. Forecast. Soc. Chang. 44, 391–404 (1993)

    Article  Google Scholar 

  20. L. Suganthi, Anand A. Samuel, Modelling and forecasting energy consumption in INDIA: Influence of socioeconomic variables. Energy Sour. Part B: Econ. Plan. Policy 11(5), 404–411 (2016). doi:10.1080/15567249.2011.631087

    Article  Google Scholar 

  21. SEMANCO Project Website, http://www.semanco-project.eu/

  22. D.L. Goodhue, R.L. Thompson, Task-technology fit and individual performance. MIS Q. 19(1), 213–236 (1995)

    Article  Google Scholar 

  23. C. Smith, J. Mentzer, Forecasting task-technology fit: the influence of individuals, systems and procedures on forecast performance. Int. J. Forecast. 26(1), 144–161 (2010)

    Article  Google Scholar 

  24. Gas Infrastructure Europe, http://www.gie.eu/index.php/publications

  25. NOAA National Centers for Environmental Information, https://www.ncdc.noaa.gov

  26. V. Adriaenssens, B. De Baets, P.L.M. Goethals, N. De Pauw, Fuzzy rule-based models for decision support in ecosystem management. Sci. Total Environ. 319, 1–12 (2004)

    Article  Google Scholar 

  27. Z. Sen, Fuzzy Logic and Foundation. (Bilge KulturSanat Publisher, Istanbul, 2001), p. 172. ISBN 9758509233

    Google Scholar 

  28. T. Takagi, M. Sugeno, Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)

    Article  MATH  Google Scholar 

  29. J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing (Prentice Hall, 1997), p. 607 ISBN 0-13-261066-3

    Google Scholar 

  30. R. Singh, A. Kainthola, T.N. Singh, Estimation of elastic constant of rocks using an ANFIS approach. Appl. Soft Comput. 12, 40–45 (2012)

    Article  Google Scholar 

  31. A. Azadeh, M. Saberi, M. Anvari, A. Azaron, M. Mohammadi, An adaptive network based fuzzy inference system–genetic algorithm clustering ensemble algorithm for performance assessment and improvement of conventional power plants. Expert Syst. Appl. 38, 2224–2234 (2011)

    Article  Google Scholar 

  32. B. Khoshnevisan, S. Rafiee, H. Mousazadeh, Application of multi-layer adaptive neuro-fuzzy inference system for estimation of greenhouse strawberry yield. Measurement 47, 903–910 (2014)

    Google Scholar 

  33. S. Haykin, Neural Networks: A Comprehensive Foundation (Prentice Hall, New Jersey, 1998)

    MATH  Google Scholar 

  34. G.P. Zhang, Neural networks for classification: a survey. IEEE Trans. Syst. Man and Cybern. Part C: Appl. Rev. 30(4), 451–462 (2000)

    Google Scholar 

  35. C.M. Bishop, Neural Networks for Pattern Recognition (Oxford University Press, Oxford H. R., 1995)

    MATH  Google Scholar 

  36. M. Firat, M. Yurdusev, M. Turan, Evaluation of artificial neural network techniques for municipal water consumption modeling. Water Resour. Manage. 23, 617–632 (2009)

    Article  Google Scholar 

  37. B. Kosko, Fuzzy cognitive maps. Int. J. Man-Mach. Stud. 24(1), 65–75 (1986). doi:10.1016/S0020-7373(86)80040-2

    Article  MATH  Google Scholar 

  38. W. Stach, L. Kurgan, W. Pedrycz, M. Reformat, Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 153(3), 371–401 (2005). doi:10.1016/j.fss.2005.01.009

    Article  MathSciNet  MATH  Google Scholar 

  39. E.I. Papageorgiou, Fuzzy Cognitive Maps for Applied Sciences and Engineering From Fundamentals to Extensions and Learning Algorithms Intelligent Systems Reference Library, vol. 54 (Springer, 2014)

    Google Scholar 

  40. E.I. Papageorgiou, W. Froelich, Multi-step prediction of pulmonary infection with the use of evolutionary fuzzy cognitive maps. Neurocomputing 92, 28–35 (2012). doi:10.1016/j.neucom.2011.08.034

    Article  Google Scholar 

  41. E.I. Papageorgiou, K. Poczeta, A. Yastrebov, Fuzzy Cognitive Maps and Multi-Step Gradient Methods for Prediction: Applications to Electricity Consumption and Stock Exchange Returns, in 7th International KES Conference on INTELLIGENT DECISION TECHNOLOGIES KES-IDT-15, Hilton Sorrento Palace, Italy, 17–19 June 2015, published by Springer in Smart Innovation Systems and Technologies, http://idt---15.kesinternational.org/cms/userfiles/is02.pdf

  42. E.I. Papageorgiou, K. Poczeta, Application of Fuzzy Cognitive Maps to Electricity Consumption Prediction, in NAFIPS 2015, Annual Conference of the North American Fuzzy Information Processing Society NAFIPS’2015, Redmond, Washington, USA, August 17–19, 2015

    Google Scholar 

  43. W. Froelich, E.I. Papageorgiou, M. Samarinas, K. Skriapas, Application of evolutionary fuzzy cognitive maps to the long-term prediction of prostate cancer. Appl. Soft Comput. 12, 3810–3817 (2012). doi:10.1016/j.asoc.2012.02.005

    Article  Google Scholar 

  44. K. Kokkinos, E. Papageorgiou, K. Poczeta, L. Papadopoulos, C. Laspidou, Soft Computing Approaches for Urban Water Demand Forecasting, in 8th International Conference KES-IDT 2016, Tenerife, Spain, 15–17 June, 2016, published by Springer in Smart Innovation Systems and Technologies

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Thessaly, Lamia, Greece

    Konstantinos Kokkinos

  2. Computer Engineering Department, Technological Educational Institute of Central Greece, 35100, Lamia, Greece

    Elpiniki Papageorgiou

  3. Electrical Engineering Department, Technological Educational Institute of Thessaly, 41110, Larissa, Greece

    Vassilios Dafopoulos & Ioannis Adritsos

Authors
  1. Konstantinos Kokkinos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elpiniki Papageorgiou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vassilios Dafopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ioannis Adritsos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Konstantinos Kokkinos .

Editor information

Editors and Affiliations

  1. VIT University , Vellore, Tamil Nadu, India

    Arun Kumar Sangaiah

  2. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs Sci N/w for Innova and Research Exc, Auburn, Washington, USA

    Ajith Abraham

  3. Faculté des Sciences et Technologie, Université Paris-Est Créteil Val-de-Marn Faculté des Sciences et Technologie, Créteil Cedex, France

    Patrick Siarry

  4. Faculty of Engig Computer & Math sci, The University of Adelaide Faculty of Engig Computer & Math sci, Adelaide, South Australia, Australia

    Michael Sheng

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Kokkinos, K., Papageorgiou, E., Dafopoulos, V., Adritsos, I. (2017). Efficiency in Energy Decision Support Systems Using Soft Computing Techniques. In: Sangaiah, A., Abraham, A., Siarry, P., Sheng, M. (eds) Intelligent Decision Support Systems for Sustainable Computing. Studies in Computational Intelligence, vol 705. Springer, Cham. https://doi.org/10.1007/978-3-319-53153-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-53153-3_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53152-6

  • Online ISBN: 978-3-319-53153-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation