IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i11p12355-13061d58949.html
   My bibliography  Save this article

Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources

Author

Listed:
  • Otilia Elena Dragomir

    (Automation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, Romania)

  • Florin Dragomir

    (Automation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, Romania)

  • Veronica Stefan

    (Automation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, Romania)

  • Eugenia Minca

    (Automation, Computer Science and Electrical Engineering Department, Valahia University of Târgoviște, 2 Carol I Bd., Targoviste 130024, Romania)

Abstract

The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users’ decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS) models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1) and the shape of membership functions (Scenario 2).

Suggested Citation

  • Otilia Elena Dragomir & Florin Dragomir & Veronica Stefan & Eugenia Minca, 2015. "Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources," Energies, MDPI, vol. 8(11), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:11:p:12355-13061:d:58949
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/11/12355/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/11/12355/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew Kusiak & Xiupeng Wei, 2014. "Prediction of methane production in wastewater treatment facility: a data-mining approach," Annals of Operations Research, Springer, vol. 216(1), pages 71-81, May.
    2. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    3. Yea-Kuang Chan & Jyh-Cherng Gu, 2012. "Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants," Energies, MDPI, vol. 5(1), pages 1-18, January.
    4. Marin Cerjan & Marin Matijaš & Marko Delimar, 2014. "Dynamic Hybrid Model for Short-Term Electricity Price Forecasting," Energies, MDPI, vol. 7(5), pages 1-15, May.
    5. Ying-Yi Hong & Yan-Hung Wei & Yung-Ruei Chang & Yih-Der Lee & Pang-Wei Liu, 2014. "Fault Detection and Location by Static Switches in Microgrids Using Wavelet Transform and Adaptive Network-Based Fuzzy Inference System," Energies, MDPI, vol. 7(4), pages 1-18, April.
    6. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    7. Mario Collotta & Antonio Messineo & Giuseppina Nicolosi & Giovanni Pau, 2014. "A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input," Energies, MDPI, vol. 7(8), pages 1-30, July.
    8. Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Lorena Calavia & Belén Carro & Antonio Sánchez-Esguevillas & Francisco Pérez & Ángel Fernández & Jaime Lloret, 2014. "Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems," Energies, MDPI, vol. 7(3), pages 1-23, March.
    9. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
    10. Jin Woo Moon & Jae D. Chang & Sooyoung Kim, 2013. "Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings," Energies, MDPI, vol. 6(7), pages 1-23, July.
    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. Yongming Zhang & Zhe Yan & Feng Yuan & Jiawei Yao & Bao Ding, 2018. "A Novel Reconstruction Approach to Elevator Energy Conservation Based on a DC Micro-Grid in High-Rise Buildings," Energies, MDPI, vol. 12(1), pages 1-17, December.
    2. Santos-Herrero, J.M. & Lopez-Guede, J.M. & Flores-Abascal, I., 2021. "Modeling, simulation and control tools for nZEB: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    3. Elnazeer Ali Hamid Abdalla & Perumal Nallagownden & Nursyarizal Bin Mohd Nor & Mohd Fakhizan Romlie & Sabo Miya Hassan, 2018. "An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus," Energies, MDPI, vol. 11(4), pages 1-24, March.
    4. Nguyen Gia Minh Thao & Kenko Uchida, 2018. "An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems," Energies, MDPI, vol. 11(3), pages 1-26, February.
    5. Yue Chen & Zhizhong Guo & Abebe Tilahun Tadie & Hongbo Li & Guizhong Wang & Yingwei Hou, 2019. "Tie-Line Reserve Power Probability Margin for Day-Ahead Dispatching in Power Systems with High Proportion Renewable Power Sources," Energies, MDPI, vol. 12(24), pages 1-23, December.
    6. Veerapandiyan Veerasamy & Noor Izzri Abdul Wahab & Rajeswari Ramachandran & Muhammad Mansoor & Mariammal Thirumeni & Mohammad Lutfi Othman, 2018. "High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 11(12), pages 1-24, November.
    7. Seung Wan Kim & Jip Kim & Young Gyu Jin & Yong Tae Yoon, 2016. "Optimal Bidding Strategy for Renewable Microgrid with Active Network Management," Energies, MDPI, vol. 9(1), pages 1-15, January.
    8. Stéfano Frizzo Stefenon & Roberto Zanetti Freire & Leandro dos Santos Coelho & Luiz Henrique Meyer & Rafael Bartnik Grebogi & William Gouvêa Buratto & Ademir Nied, 2020. "Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System," Energies, MDPI, vol. 13(2), pages 1-19, January.
    9. Quan Zhou & Taotao Xiong & Mubin Wang & Chenmeng Xiang & Qingpeng Xu, 2017. "Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS," Energies, MDPI, vol. 10(7), pages 1-15, July.
    10. Iulia Stamatescu & Nicoleta Arghira & Ioana Făgărăşan & Grigore Stamatescu & Sergiu Stelian Iliescu & Vasile Calofir, 2017. "Decision Support System for a Low Voltage Renewable Energy System," Energies, MDPI, vol. 10(1), pages 1-15, January.

    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. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    2. Stefanescu, Răzvan & Dumitriu, Ramona, 2017. "Ajustarea seriilor de timp financiare,Partea întâi [Smoothing of financial time series, Part 1]," MPRA Paper 78329, University Library of Munich, Germany, revised 15 Apr 2017.
    3. Madden, Gary & Tan, Joachim, 2007. "Forecasting telecommunications data with linear models," Telecommunications Policy, Elsevier, vol. 31(1), pages 31-44, February.
    4. Garcia-Ferrer, Antonio & Bujosa-Brun, Marcos, 2000. "Forecasting OECD industrial turning points using unobserved components models with business survey data," International Journal of Forecasting, Elsevier, vol. 16(2), pages 207-227.
    5. Kumar, V. & Leone, Robert P. & Gaskins, John N., 1995. "Aggregate and disaggregate sector forecasting using consumer confidence measures," International Journal of Forecasting, Elsevier, vol. 11(3), pages 361-377, September.
    6. repec:lan:wpaper:470 is not listed on IDEAS
    7. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    8. Konrad Bogner & Katharina Liechti & Luzi Bernhard & Samuel Monhart & Massimiliano Zappa, 2018. "Skill of Hydrological Extended Range Forecasts for Water Resources Management in Switzerland," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 969-984, February.
    9. Garcia-Ferrer, Antonio & Queralt, Ricardo A., 1997. "A note on forecasting international tourism demand in Spain," International Journal of Forecasting, Elsevier, vol. 13(4), pages 539-549, December.
    10. Shah, Chandra, 1997. "Model selection in univariate time series forecasting using discriminant analysis," International Journal of Forecasting, Elsevier, vol. 13(4), pages 489-500, December.
    11. Wagatha, Matthias, 2007. "Integration, Kointegration und die Langzeitprognose von Kreditausfallzyklen [Integration, Cointegration and Long-Horizont Forecasting of Credit-Default-Cycles]," MPRA Paper 8602, University Library of Munich, Germany.
    12. Tashman, Leonard J. & Kruk, Joshua M., 1996. "The use of protocols to select exponential smoothing procedures: A reconsideration of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 12(2), pages 235-253, June.
    13. Bruce G. S. Hardie & Peter S. Fader & Robert Zeithammer, 2003. "Forecasting new product trial in a controlled test market environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 391-410.
    14. Bunn, Derek W. & Taylor, James W., 2001. "Setting accuracy targets for short-term judgemental sales forecasting," International Journal of Forecasting, Elsevier, vol. 17(2), pages 159-169.
    15. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    16. Oliver Schaer & Nikolaos Kourentzes & Robert Fildes, 2022. "Predictive competitive intelligence with prerelease online search traffic," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3823-3839, October.
    17. Thury, Gerhard & Witt, Stephen F., 1998. "Forecasting industrial production using structural time series models," Omega, Elsevier, vol. 26(6), pages 751-767, December.
    18. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.
    19. Fildes, Robert & Hibon, Michele & Makridakis, Spyros & Meade, Nigel, 1998. "Generalising about univariate forecasting methods: further empirical evidence," International Journal of Forecasting, Elsevier, vol. 14(3), pages 339-358, September.
    20. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    21. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.

    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:jeners:v:8:y:2015:i:11:p:12355-13061:d:58949. 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.