IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i11p3204-d565753.html
   My bibliography  Save this article

Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Author

Listed:
  • Michał Sabat

    (Electrical Power Engineering Institute, Warsaw University of Technology, Building of Mechanics, ul. Koszykowa 75, 00-662 Warszawa, Poland)

  • Dariusz Baczyński

    (Electrical Power Engineering Institute, Warsaw University of Technology, Building of Mechanics, ul. Koszykowa 75, 00-662 Warszawa, Poland)

Abstract

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

Suggested Citation

  • Michał Sabat & Dariusz Baczyński, 2021. "Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case," Energies, MDPI, vol. 14(11), pages 1-19, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3204-:d:565753
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/11/3204/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/11/3204/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huang, Jianhua & Gurney, Kevin Robert, 2016. "The variation of climate change impact on building energy consumption to building type and spatiotemporal scale," Energy, Elsevier, vol. 111(C), pages 137-153.
    2. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    3. Jianhua Huang & Kevin Robert Gurney, 2016. "Impact of climate change on U.S. building energy demand: sensitivity to spatiotemporal scales, balance point temperature, and population distribution," Climatic Change, Springer, vol. 137(1), pages 171-185, July.
    4. Gangjun Gong & Xiaonan An & Nawaraj Kumar Mahato & Shuyan Sun & Si Chen & Yafeng Wen, 2019. "Research on Short-Term Load Prediction Based on Seq2seq Model," Energies, MDPI, vol. 12(16), pages 1-18, August.
    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. Burleyson, Casey D. & Voisin, Nathalie & Taylor, Z. Todd & Xie, Yulong & Kraucunas, Ian, 2018. "Simulated building energy demand biases resulting from the use of representative weather stations," Applied Energy, Elsevier, vol. 209(C), pages 516-528.
    2. Craig, Michael T. & Cohen, Stuart & Macknick, Jordan & Draxl, Caroline & Guerra, Omar J. & Sengupta, Manajit & Haupt, Sue Ellen & Hodge, Bri-Mathias & Brancucci, Carlo, 2018. "A review of the potential impacts of climate change on bulk power system planning and operations in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 255-267.
    3. Burleyson, Casey D. & Iyer, Gokul & Hejazi, Mohamad & Kim, Sonny & Kyle, Page & Rice, Jennie S. & Smith, Amanda D. & Taylor, Z. Todd & Voisin, Nathalie & Xie, Yulong, 2020. "Future western U.S. building electricity consumption in response to climate and population drivers: A comparative study of the impact of model structure," Energy, Elsevier, vol. 208(C).
    4. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    5. Fei Teng & Yafei Song & Xinpeng Guo, 2021. "Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model," Mathematics, MDPI, vol. 9(19), pages 1-21, September.
    6. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
    7. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    8. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation," Energies, MDPI, vol. 12(18), pages 1-19, September.
    9. Sepehr Moalem & Roya M. Ahari & Ghazanfar Shahgholian & Majid Moazzami & Seyed Mohammad Kazemi, 2022. "Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach," Energies, MDPI, vol. 15(21), pages 1-17, October.
    10. Jungwon Yoon & Sanghyun Bae, 2020. "Performance Evaluation and Design of Thermo-Responsive SMP Shading Prototypes," Sustainability, MDPI, vol. 12(11), pages 1-35, May.
    11. Filippín, Celina & Ricard, Florencia & Flores Larsen, Silvana & Santamouris, Mattheos, 2017. "Retrospective analysis of the energy consumption of single-family dwellings in central Argentina. Retrofitting and adaptation to the climate change," Renewable Energy, Elsevier, vol. 101(C), pages 1226-1241.
    12. Bulent Haznedar & Huseyin Cagan Kilinc & Furkan Ozkan & Adem Yurtsever, 2023. "Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 681-701, May.
    13. Kanitta Yarak & Apichon Witayangkurn & Kunnaree Kritiyutanont & Chomchanok Arunplod & Ryosuke Shibasaki, 2021. "Oil Palm Tree Detection and Health Classification on High-Resolution Imagery Using Deep Learning," Agriculture, MDPI, vol. 11(2), pages 1-16, February.
    14. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    15. Chen, Guangwu & Zhu, Yuhan & Wiedmann, Thomas & Yao, Lina & Xu, Lixiao & Wang, Yafei, 2019. "Urban-rural disparities of household energy requirements and influence factors in China: Classification tree models," Applied Energy, Elsevier, vol. 250(C), pages 1321-1335.
    16. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
    17. Soltanisarvestani, A. & Safavi, A.A., 2021. "Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map," Utilities Policy, Elsevier, vol. 72(C).
    18. Song, Wanqing & Cattani, Carlo & Chi, Chi-Hung, 2020. "Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach," Energy, Elsevier, vol. 194(C).
    19. Myoungsoo Kim & Wonik Choi & Youngjun Jeon & Ling Liu, 2019. "A Hybrid Neural Network Model for Power Demand Forecasting," Energies, MDPI, vol. 12(5), pages 1-17, March.
    20. Jiil Kim & Cheong Hee Park, 2020. "Partial Discharge Detection Based on Anomaly Pattern Detection," Energies, MDPI, vol. 13(20), pages 1-11, October.

    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:14:y:2021:i:11:p:3204-:d:565753. 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.