IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i8p1329-d795708.html
   My bibliography  Save this article

A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models

Author

Listed:
  • Madeline Hui Li Lee

    (Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, Kuala Lumpur 56000, Malaysia)

  • Yee Chee Ser

    (Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, Kuala Lumpur 56000, Malaysia)

  • Ganeshsree Selvachandran

    (Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, Kuala Lumpur 56000, Malaysia)

  • Pham Huy Thong

    (VNU Information Technology Institute, Vietnam National University, Hanoi 03000, Vietnam)

  • Le Cuong

    (VNU Information Technology Institute, Vietnam National University, Hanoi 03000, Vietnam)

  • Le Hoang Son

    (VNU Information Technology Institute, Vietnam National University, Hanoi 03000, Vietnam)

  • Nguyen Trung Tuan

    (School of Information Technology and Digital Economics, National Economic University, Hanoi 100000, Vietnam)

  • Vassilis C. Gerogiannis

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

Abstract

Production of electricity from the burning of fossil fuels has caused an increase in the emission of greenhouse gases. In the long run, greenhouse gases cause harm to the environment. To reduce these gases, it is important to accurately forecast electricity production, supply and consumption. Forecasting of electricity consumption is, in particular, useful for minimizing problems of overproduction and oversupply of electricity. This research study focuses on forecasting electricity consumption based on time series data using different artificial intelligence and metaheuristic methods. The aim of the study is to determine which model among the artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), least squares support vector machines (LSSVMs) and fuzzy time series (FTS) produces the highest level of accuracy in forecasting electricity consumption. The variables considered in this research include the monthly electricity consumption over the years for different countries. The monthly electricity consumption data for seven countries, namely, Norway, Switzerland, Malaysia, Egypt, Algeria, Bulgaria and Kenya, for 10 years were used in this research. The performance of all of the models was evaluated and compared using error metrics such as the root mean squared error (RMSE), average forecasting error (AFE) and performance parameter (PP). The differences in the results obtained via the different methods are analyzed and discussed, and it is shown that the different models performed better for different countries in different forecasting periods. Overall, it was found that the FTS model performed the best for most of the countries studied compared to the other three models. The research results can allow electricity management companies to have better strategic planning when deciding on the optimal levels of electricity production and supply, with the overall aim of preventing surpluses or shortages in the electricity supply.

Suggested Citation

  • Madeline Hui Li Lee & Yee Chee Ser & Ganeshsree Selvachandran & Pham Huy Thong & Le Cuong & Le Hoang Son & Nguyen Trung Tuan & Vassilis C. Gerogiannis, 2022. "A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1329-:d:795708
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/8/1329/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/8/1329/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Akdi, Yılmaz & Gölveren, Elif & Okkaoğlu, Yasin, 2020. "Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting," Energy, Elsevier, vol. 191(C).
    2. Ardakani, F.J. & Ardehali, M.M., 2014. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types," Energy, Elsevier, vol. 65(C), pages 452-461.
    3. Bangzhu Zhu & Julien Chevallier, 2017. "Pricing and Forecasting Carbon Markets," Springer Books, Springer, number 978-3-319-57618-3, April.
    4. Yu, Hui-Kuang, 2005. "Weighted fuzzy time series models for TAIEX forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 609-624.
    5. Zuhaimy Ismail & Riswan Efendi & Mustafa Mat Deris, 2015. "Application of Fuzzy Time Series Approach in Electric Load Forecasting," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 11(03), pages 229-248.
    6. Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
    7. Nur Rahman & Muhammad Lee & Suhartono & Mohd Latif, 2015. "Artificial neural networks and fuzzy time series forecasting: an application to air quality," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2633-2647, November.
    8. Yuan, Xiaohui & Tan, Qingxiong & Lei, Xiaohui & Yuan, Yanbin & Wu, Xiaotao, 2017. "Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine," Energy, Elsevier, vol. 129(C), pages 122-137.
    9. Zhu Bangzhu & Julien Chevallier, 2017. "Pricing and Forecasting Carbon Markets: Models and Empirical Analyses," Post-Print hal-02879366, HAL.
    10. Fatma Başoğlu Kabran & Kamil Demirberk Ünlü, 2021. "A two-step machine learning approach to predict S&P 500 bubbles," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(13-15), pages 2776-2794, November.
    11. Ali Azadeh & Ali Narimani & Tayebeh Nazari, 2015. "Estimating household electricity consumption by environmental consciousness," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 15(1), pages 1-19.
    12. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
    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. Jin, Haowei & Guo, Jue & Tang, Lei & Du, Pei, 2024. "Long-term electricity demand forecasting under low-carbon energy transition: Based on the bidirectional feedback between power demand and generation mix," Energy, Elsevier, vol. 286(C).
    2. Chenhua Xu & Zhicheng Tu & Wenjie Zhang & Jian Cen & Jianbin Xiong & Na Wang, 2022. "A Method of Optimizing Cell Voltage Based on STA-LSSVM Model," Mathematics, MDPI, vol. 10(24), pages 1-20, December.
    3. Siyu Zhang & Liusan Wu & Ming Cheng & Dongqing Zhang, 2022. "Prediction of Whole Social Electricity Consumption in Jiangsu Province Based on Metabolic FGM (1, 1) Model," Mathematics, MDPI, vol. 10(11), pages 1-14, May.
    4. Marwa Salah EIDin Fahmy & Farhan Ahmed & Farah Durani & Štefan Bojnec & Mona Mohamed Ghareeb, 2023. "Predicting Electricity Consumption in the Kingdom of Saudi Arabia," Energies, MDPI, vol. 16(1), pages 1-20, 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. Emmanuel Senyo Fianu, 2022. "Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach," Forecasting, MDPI, vol. 4(2), pages 1-27, June.
    2. Weijia Shao & Lukas Friedemann Radke & Fikret Sivrikaya & Sahin Albayrak, 2021. "Adaptive Online Learning for the Autoregressive Integrated Moving Average Models," Mathematics, MDPI, vol. 9(13), pages 1-30, June.
    3. Liudmila Reshetnikova & Natalia Boldyreva & Anton Devyatkov & Zhanna Pisarenko & Danila Ovechkin, 2023. "Carbon Pricing in Current Global Institutional Changes," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    4. Fu, Wenlong & Fang, Ping & Wang, Kai & Li, Zhenxing & Xiong, Dongzhen & Zhang, Kai, 2021. "Multi-step ahead short-term wind speed forecasting approach coupling variational mode decomposition, improved beetle antennae search algorithm-based synchronous optimization and Volterra series model," Renewable Energy, Elsevier, vol. 179(C), pages 1122-1139.
    5. Bangzhu Zhu & Shunxin Ye & Ping Wang & Julien Chevallier & Yi‐Ming Wei, 2022. "Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 100-117, January.
    6. López, Germánico & Arboleya, Pablo, 2022. "Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador," Renewable Energy, Elsevier, vol. 183(C), pages 351-368.
    7. García, Irene & Huo, Stella & Prado, Raquel & Bravo, Lelys, 2020. "Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements," Renewable Energy, Elsevier, vol. 161(C), pages 55-64.
    8. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    9. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    10. Farrokhifar, Meisam & Nie, Yinghui & Pozo, David, 2020. "Energy systems planning: A survey on models for integrated power and natural gas networks coordination," Applied Energy, Elsevier, vol. 262(C).
    11. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.
    12. Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
    13. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    14. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    15. Karpf, Andreas & Mandel, Antoine & Battiston, Stefano, 2018. "Price and network dynamics in the European carbon market," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 103-122.
    16. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
    17. Cheng, Ching-Hsue & Wei, Liang-Ying, 2014. "A novel time-series model based on empirical mode decomposition for forecasting TAIEX," Economic Modelling, Elsevier, vol. 36(C), pages 136-141.
    18. Wang, Lei & Wang, Xinyu & Zhao, Zhongchao, 2024. "Mid-term electricity demand forecasting using improved multi-mode reconstruction and particle swarm-enhanced support vector regression," Energy, Elsevier, vol. 304(C).
    19. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
    20. Ghaem Sigarchian, Sara & Paleta, Rita & Malmquist, Anders & Pina, André, 2015. "Feasibility study of using a biogas engine as backup in a decentralized hybrid (PV/wind/battery) power generation system – Case study Kenya," Energy, Elsevier, vol. 90(P2), pages 1830-1841.

    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:10:y:2022:i:8:p:1329-:d:795708. 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.