IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4870-d415090.html
   My bibliography  Save this article

Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources

Author

Listed:
  • Prince Waqas Khan

    (Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea)

  • Yung-Cheol Byun

    (Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea)

  • Sang-Joon Lee

    (Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea)

  • Dong-Ho Kang

    (Power Technology Development Team, HODI, Mapo Ssangyong Geum Building 3f, Mapo-gu, Seoul 04178, Korea)

  • Jin-Young Kang

    (Jeju Regional Headquarter, Korea Power Exchange, 81, Ora-NamRo, Jeju 63144, Korea)

  • Hae-Su Park

    (Jeju Regional Headquarter, Korea Power Exchange, 81, Ora-NamRo, Jeju 63144, Korea)

Abstract

In today’s world, renewable energy sources are increasingly integrated with nonrenewable energy sources into electric grids and pose new challenges because of their intermittent and variable nature. Energy prediction using soft-computing techniques plays a vital role in addressing these challenges. As electricity consumption is closely linked to other energy sources such as natural gas and oil, forecasting electricity consumption is essential for making national energy policies. In this paper, we utilize various data mining techniques, including preprocessing historical load data and the load time series’s characteristics. We analyzed the power consumption trends from renewable energy sources and nonrenewable energy sources and combined them. A novel machine learning-based hybrid approach, combining multilayer perceptron (MLP), support vector regression (SVR), and CatBoost, is proposed in this paper for power forecasting. A thorough comparison is made, taking into account the results obtained using other prediction methods.

Suggested Citation

  • Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Dong-Ho Kang & Jin-Young Kang & Hae-Su Park, 2020. "Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources," Energies, MDPI, vol. 13(18), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4870-:d:415090
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4870/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/18/4870/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jungwon Yu & June Ho Park & Sungshin Kim, 2018. "A New Input Selection Algorithm Using the Group Method of Data Handling and Bootstrap Method for Support Vector Regression Based Hourly Load Forecasting," Energies, MDPI, vol. 11(11), pages 1-20, October.
    2. María Pérez-Ortiz & Silvia Jiménez-Fernández & Pedro A. Gutiérrez & Enrique Alexandre & César Hervás-Martínez & Sancho Salcedo-Sanz, 2016. "A Review of Classification Problems and Algorithms in Renewable Energy Applications," Energies, MDPI, vol. 9(8), pages 1-27, August.
    3. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    4. Rui Wang & Jingrui Li & Jianzhou Wang & Chengze Gao, 2018. "Research and Application of a Hybrid Wind Energy Forecasting System Based on Data Processing and an Optimized Extreme Learning Machine," Energies, MDPI, vol. 11(7), pages 1-29, July.
    5. Ewing, Bradley T. & Payne, James E. & Caporin, Massimilano, 2022. "The Asymmetric Impact of Oil Prices and Production on Drilling Rig Trajectory: A correction," Resources Policy, Elsevier, vol. 79(C).
    6. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Namje Park, 2020. "Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting," Energies, MDPI, vol. 13(11), pages 1-23, May.
    7. Gabriel Mendonça de Paiva & Sergio Pires Pimentel & Bernardo Pinheiro Alvarenga & Enes Gonçalves Marra & Marco Mussetta & Sonia Leva, 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks," Energies, MDPI, vol. 13(11), pages 1-28, June.
    8. Hannah Mareike Marczinkowski & Poul Alberg Østergaard & Søren Roth Djørup, 2019. "Transitioning Island Energy Systems—Local Conditions, Development Phases, and Renewable Energy Integration," Energies, MDPI, vol. 12(18), pages 1-20, September.
    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. Behnam Talebjedi & Ali Khosravi & Timo Laukkanen & Henrik Holmberg & Esa Vakkilainen & Sanna Syri, 2020. "Energy Modeling of a Refiner in Thermo-Mechanical Pulping Process Using ANFIS Method," Energies, MDPI, vol. 13(19), pages 1-26, October.
    2. Can Ding & Yiyuan Zhou & Qingchang Ding & Kaiming Li, 2022. "Integrated Carbon-Capture-Based Low-Carbon Economic Dispatch of Power Systems Based on EEMD-LSTM-SVR Wind Power Forecasting," Energies, MDPI, vol. 15(5), pages 1-27, February.
    3. Yukta Mehta & Rui Xu & Benjamin Lim & Jane Wu & Jerry Gao, 2023. "A Review for Green Energy Machine Learning and AI Services," Energies, MDPI, vol. 16(15), pages 1-30, July.
    4. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    5. Prince Waqas Khan & Yongjun Kim & Yung-Cheol Byun & Sang-Joon Lee, 2021. "Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction," Energies, MDPI, vol. 14(21), pages 1-22, November.
    6. Jiyuan Zhang & Qihong Feng & Xianmin Zhang & Qiujia Hu & Jiaosheng Yang & Ning Wang, 2020. "A Novel Data-Driven Method to Estimate Methane Adsorption Isotherm on Coals Using the Gradient Boosting Decision Tree: A Case Study in the Qinshui Basin, China," Energies, MDPI, vol. 13(20), pages 1-21, October.

    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. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
    2. Chan Roh, 2022. "Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators," Energies, MDPI, vol. 15(9), pages 1-18, April.
    3. Ismael Pérez-Franco & Agustín García-García & Juan J. Maldonado-Briegas, 2020. "Energy Transition Towards a Greener and More Competitive Economy: The Iberian Case," Sustainability, MDPI, vol. 12(8), pages 1-14, April.
    4. Ranjan Aneja & Umer J. Banday & Tanzeem Hasnat & Mustafa Koçoglu, 2017. "Renewable and Non-renewable Energy Consumption and Economic Growth: Empirical Evidence from Panel Error Correction Model," Jindal Journal of Business Research, , vol. 6(1), pages 76-85, June.
    5. Al-mulali, Usama & Fereidouni, Hassan Gholipour & Lee, Janice Y.M., 2014. "Electricity consumption from renewable and non-renewable sources and economic growth: Evidence from Latin American countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 290-298.
    6. Garrod Brian & Almeida António & Machado Luiz, 2023. "Modelling of nonlinear asymmetric effects of changes in tourism on economic growth in an autonomous small-island economy," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 13(2), pages 154-172, December.
    7. Omri, Anis, 2014. "An international literature survey on energy-economic growth nexus: Evidence from country-specific studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 951-959.
    8. Apergis, Nicholas & Payne, James E., 2010. "Energy consumption and growth in South America: Evidence from a panel error correction model," Energy Economics, Elsevier, vol. 32(6), pages 1421-1426, November.
    9. Wan-Lin Yong & Jerome Kueh & Yong Sze Wei & Jang-Haw Tiang, 2020. "Energy Consumption and Economic Growth Nexus in China: Autoregressive Distributed Lag (ARDL)," Journal of Public Administration and Governance, Macrothink Institute, vol. 10(2), pages 194212-1942, December.
    10. Apergis, Nicholas & Payne, James E., 2010. "Coal consumption and economic growth: Evidence from a panel of OECD countries," Energy Policy, Elsevier, vol. 38(3), pages 1353-1359, March.
    11. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    12. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    13. Bilgili, Faik & Mugaloglu, Erhan & Koçak, Emrah, 2018. "The impact of oil prices on CO2 emissions in China: A Wavelet coherence approach," MPRA Paper 90170, University Library of Munich, Germany.
    14. Mehdi Abid & Rafaa Mraihi, 2015. "Energy Consumption and Industrial Production: Evidence from Tunisia at Both Aggregated and Disaggregated Levels," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 6(4), pages 1123-1137, December.
    15. Małgorzata Sztorc, 2022. "The Implementation of the European Green Deal Strategy as a Challenge for Energy Management in the Face of the COVID-19 Pandemic," Energies, MDPI, vol. 15(7), pages 1-21, April.
    16. Inglesi-Lotz, Roula, 2016. "The impact of renewable energy consumption to economic growth: A panel data application," Energy Economics, Elsevier, vol. 53(C), pages 58-63.
    17. Hoang Phong Le & Ho Hoang Gia Bao, 2020. "Renewable and Nonrenewable Energy Consumption, Government Expenditure, Institution Quality, Financial Development, Trade Openness, and Sustainable Development in Latin America and Caribbean Emerging M," International Journal of Energy Economics and Policy, Econjournals, vol. 10(1), pages 242-248.
    18. Liddle, Brantley, 2013. "Population, Affluence, and Environmental Impact Across Development: Evidence from Panel Cointegration Modeling," MPRA Paper 52088, University Library of Munich, Germany.
    19. Oosthuizen, Anna Maria & Inglesi-Lotz, Roula & Thopil, George Alex, 2022. "The relationship between renewable energy and retail electricity prices: Panel evidence from OECD countries," Energy, Elsevier, vol. 238(PB).
    20. Gerard Bikorimana & Charles Rutikanga & Didier Mwizerwa, 2020. "Linking energy consumption with economic growth: Rwanda as a case study," ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, FrancoAngeli Editore, vol. 2020(2), pages 181-200.

    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:13:y:2020:i:18:p:4870-:d:415090. 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.