IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v137y2015icp588-602.html
   My bibliography  Save this article

A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the Support Vector Machine

Author

Listed:
  • Chia, Yen Yee
  • Lee, Lam Hong
  • Shafiabady, Niusha
  • Isa, Dino

Abstract

This paper presents the use of a Support Vector Machine load predictive energy management system to control the energy flow between a solar energy source, a supercapacitor-battery hybrid energy storage combination and the load. The supercapacitor-battery hybrid energy storage system is deployed in a solar energy system to improve the reliability of delivered power. The combination of batteries and supercapacitors makes use of complementary characteristic that allow the overlapping of a battery’s high energy density with a supercapacitors’ high power density. This hybrid system produces a straightforward benefit over either individual system, by taking advantage of each characteristic. When the supercapacitor caters for the instantaneous peak power which prolongs the battery lifespan, it also minimizes the system cost and ensures a greener system by reducing the number of batteries. The resulting performance is highly dependent on the energy controls implemented in the system to exploit the strengths of the energy storage devices and minimize its weaknesses. It is crucial to use energy from the supercapacitor and therefore minimize jeopardizing the power system reliability especially when there is a sudden peak power demand. This study has been divided into two stages. The first stage is to obtain the optimum SVM load prediction model, and the second stage carries out the performance comparison of the proposed SVM-load predictive energy management system with conventional sequential programming control (if-else condition). An optimized load prediction classification model is investigated and implemented. This C-Support Vector Classification yields classification accuracy of 100% using 17 support vectors in 0.004866s of training time. The Polynomial kernel is the optimum kernel in our experiments where the C and g values are 2 and 0.25 respectively. However, for the load profile regression model which was implemented in the K-step ahead of load prediction, the radial basis function (RBF) kernel was chosen due to the highest squared correlation coefficient and the lowest mean squared error. Results obtained shows that the proposed SVM load predictive energy management system accurately identifies and predicts the load demand. This has been justified by the supercapacitor charging and leading the peak current demand by 200ms for different load profiles with different optimized regression models. This methodology optimizes the cost of the system by reducing the amount of power electronics within the hybrid energy storage system, and also prolongs the batteries’ lifespan as previously mentioned.

Suggested Citation

  • Chia, Yen Yee & Lee, Lam Hong & Shafiabady, Niusha & Isa, Dino, 2015. "A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the Support Vector Machine," Applied Energy, Elsevier, vol. 137(C), pages 588-602.
    • Handle: RePEc:eee:appene:v:137:y:2015:i:c:p:588-602
    DOI: 10.1016/j.apenergy.2014.09.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261914009738
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2014.09.026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    2. repec:cdl:itsdav:qt9n905017 is not listed on IDEAS
    3. Lee, Sang C. & Kwon, Osung & Thomas, Sobi & Park, Sam & Choi, Gyeung-Ho, 2014. "Graphical and mathematical analysis of fuel cell/battery passive hybridization with K factors," Applied Energy, Elsevier, vol. 114(C), pages 135-145.
    4. Esen, Hikmet & Inalli, Mustafa & Sengur, Abdulkadir & Esen, Mehmet, 2008. "Modeling a ground-coupled heat pump system by a support vector machine," Renewable Energy, Elsevier, vol. 33(8), pages 1814-1823.
    Full references (including those not matched with items on IDEAS)

    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. Andreas Lenk & Marcus Vogt & Christoph Herrmann, 2024. "An Approach to Predicting Energy Demand Within Automobile Production Using the Temporal Fusion Transformer Model," Energies, MDPI, vol. 18(1), pages 1-34, December.
    2. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    3. Lee, C.K., 2011. "Effects of multiple ground layers on thermal response test analysis and ground-source heat pump simulation," Applied Energy, Elsevier, vol. 88(12), pages 4405-4410.
    4. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
    5. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    6. Dong, Shengming & Zhang, Yufeng & He, Zhonglu & Deng, Na & Yu, Xiaohui & Yao, Sheng, 2018. "Investigation of Support Vector Machine and Back Propagation Artificial Neural Network for performance prediction of the organic Rankine cycle system," Energy, Elsevier, vol. 144(C), pages 851-864.
    7. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
    8. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    9. Rana Loubani & Didier Defer & Ola Alhaj-Hasan & Julien Chamoin, 2025. "Optimization of Hydronic Heating System in a Commercial Building: Application of Predictive Control with Limited Data," Energies, MDPI, vol. 18(9), pages 1-25, April.
    10. Jong-Wook Kim & Heungju Ahn & Hyeon Cheol Seo & Sang Cheol Lee, 2022. "Optimization of Solar/Fuel Cell Hybrid Energy System Using the Combinatorial Dynamic Encoding Algorithm for Searches (cDEAS)," Energies, MDPI, vol. 15(8), pages 1-15, April.
    11. Afroz, Zakia & Urmee, Tania & Shafiullah, G.M. & Higgins, Gary, 2018. "Real-time prediction model for indoor temperature in a commercial building," Applied Energy, Elsevier, vol. 231(C), pages 29-53.
    12. Liu, Yanli & Wang, Junyi & Liu, Liqi, 2024. "Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events," Applied Energy, Elsevier, vol. 376(PA).
    13. Mohammad Nikoo & Akbar Karimi & Reza Kerachian & Hamed Poorsepahy-Samian & Farhang Daneshmand, 2013. "Rules for Optimal Operation of Reservoir-River-Groundwater Systems Considering Water Quality Targets: Application of M5P Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2771-2784, June.
    14. Zanchini, Enzo & Lazzari, Stefano & Priarone, Antonella, 2012. "Long-term performance of large borehole heat exchanger fields with unbalanced seasonal loads and groundwater flow," Energy, Elsevier, vol. 38(1), pages 66-77.
    15. Sun, Chunhua & Zhang, Haixiang & Cao, Shanshan & Xia, Guoqiang & Zhong, Jian & Wu, Xiangdong, 2023. "A hierarchical classifying and two-step training strategy for detection and diagnosis of anormal temperature in district heating system," Applied Energy, Elsevier, vol. 349(C).
    16. Muhammad Waseem Ahmad & Anthony Mouraud & Yacine Rezgui & Monjur Mourshed, 2018. "Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption," Energies, MDPI, vol. 11(12), pages 1-21, December.
    17. Gang, Wenjie & Wang, Jinbo, 2013. "Predictive ANN models of ground heat exchanger for the control of hybrid ground source heat pump systems," Applied Energy, Elsevier, vol. 112(C), pages 1146-1153.
    18. Sebarchievici, Calin & Sarbu, Ioan, 2015. "Performance of an experimental ground-coupled heat pump system for heating, cooling and domestic hot-water operation," Renewable Energy, Elsevier, vol. 76(C), pages 148-159.
    19. Luo, Na & Hong, Tianzhen & Li, Hui & Jia, Ruoxi & Weng, Wenguo, 2017. "Data analytics and optimization of an ice-based energy storage system for commercial buildings," Applied Energy, Elsevier, vol. 204(C), pages 459-475.
    20. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:appene:v:137:y:2015:i:c:p:588-602. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.