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        <journal-title xml:lang="en">Journal of Discovery Core</journal-title>
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        <publisher-name>Digital Intelligence Press</publisher-name>
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      <title-group>
        <article-title xml:lang="en">&lt;bold&gt;Research on an Energy Economic Demand Time Series Prediction Model Based on the Integration of XGBoost and Deep Learning&lt;/bold&gt;</article-title>
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            <name name-style="western" specific-use="primary">
              <surname>Chen</surname>
              <given-names>Guona</given-names>
            </name>
          </name-alternatives>
          <email>cgn@st.cupk.edu.cn</email>
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        <institution content-type="orgname">School of Business Administration/School of Marxism, China University of Petroleum-Beijing at Karamay, Karamay, Xinjiang, China</institution>
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        <year>2026</year>
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          <event-desc>Received: <date date-type="received" iso-8601-date="2026-07-08T23:48:28+00:00"><day>8</day><month>7</month><year>2026</year></date></event-desc>
        </event>
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      <permissions>
        <copyright-statement>Copyright (c) 2026 Guona Chen (Author)</copyright-statement>
        <copyright-year>2026</copyright-year>
        <copyright-holder>Guona Chen (Author)</copyright-holder>
        <license xlink:href="https://creativecommons.org/licenses/by/4.0">
          <license-p>&lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;&lt;img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /&gt;&lt;/a&gt;&lt;p&gt;This work is licensed under a &lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;Creative Commons Attribution 4.0 International License&lt;/a&gt;.&lt;/p&gt;</license-p>
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        <kwd>Energy economic demand forecasting; Time series prediction; XGBoost; Deep learning; Ensemble model</kwd>
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    <p>

  
    
      JDC
      
        Journal of Discovery Core
        JDC
      
      
        Digital Intelligence Press
        Hong KongHKhttps://dipscie.com/index.php/JDC/index
      
      3135-7687
      
    
    
      11
      
        
          Articles
        
      
      
        &lt;bold&gt;Research on an Energy Economic Demand Time Series Prediction Model Based on the Integration of XGBoost and Deep Learning&lt;/bold&gt;
      
      
        
          
            
              Chen
              Guona
            
          
          cgn@st.cupk.edu.cn
          
        
      
      
        School of Business Administration/School of Marxism, China University of Petroleum-Beijing at Karamay, Karamay, Xinjiang, China
      
      
        09
        07
        2026
      
      1
      18
      
        
          Received: 872026
        
      
      
        Copyright (c) 2026 Guona Chen (Author)
        2026
        Guona Chen (Author)
        
          &lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;&lt;img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /&gt;&lt;/a&gt;&lt;p&gt;This work is licensed under a &lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;Creative Commons Attribution 4.0 International License&lt;/a&gt;.&lt;/p&gt;
        
      
      
      
        Energy economic demand forecasting; Time series prediction; XGBoost; Deep learning; Ensemble model
      
      
        
      
      
    
  
  
  
    
      
        [1] Alarcón, R. G., Alarcón, M. A., González, A. H., &amp; Ferramosca, A. (2025). Artificial Neural Networks for Energy Demand Prediction in an Economic MPC‐Based Energy Management System. International Journal of Robust and Nonlinear Control, 35(2), 642-658. https://doi.org/10.1002/rnc.7671
      
      
        [2] Karpavicius, T., Balezentis, T., &amp; Streimikiene, D. (2025). Energy security indicators for sustainable energy development: Application to electricity sector in the context of state economic decisions. Sustainable Development, 33(1), 1381-1400. https://doi.org/10.1002/sd.3190
      
      
        [3] Aderibigbe, A. O., Ani, E. C., Ohenhen, P. E., Ohalete, N. C., &amp; Daraojimba, D. O. (2023). Enhancing energy efficiency with ai: a review of machine learning models in electricity demand forecasting. Engineering Science &amp; Technology Journal, 4(6), 341-356. https://doi.org/10.51594/estj.v4i6.636
      
      
        [4] Szostek, K., Mazur, D., Drałus, G., &amp; Kusznier, J. (2024). Analysis of the effectiveness of ARIMA, SARIMA, and SVR models in time series forecasting: A case study of wind farm energy production. Energies, 17(19), 4803. https://doi.org/10.3390/en17194803
      
      
        [5] Jain, R., Khairnar, A. S., &amp; Karthikeyan, S. P. (2024, October). Forecasting Indian Conventional Generation: A Comparative Analysis of ARIMA and SARIMA Models for Improved Energy Demand Prediction. In 2024 International Conference on Sustainable Energy: Energy Transition and Net-Zero Climate Future (ICUE) (pp. 1-8). IEEE. https://doi.org/10.1109/ICUE63019.2024.10795531
      
      
        [6] Abbasimehr, H., Paki, R., &amp; Bahrini, A. (2023). A novel XGBoost-based featurization approach to forecast renewable energy consumption with deep learning models. Sustainable Computing: Informatics and Systems, 38, 100863. https://doi.org/10.1016/j.suscom.2023.100863
      
      
        [7] Zhang, T., Zhang, X., Liu, Y., Chow, Y. H., Iu, H. H., &amp; Fernando, T. (2023). Long-term energy and peak power demand forecasting based on sequential-XGBoost. IEEE Transactions on Power Systems, 39(2), 3088-3104. https://doi.org/10.1109/TPWRS.2023.3289400
      
      
        [8] Zhang, L., &amp; Jánošík, D. (2024). Enhanced short-term load forecasting with hybrid machine learning models: CatBoost and XGBoost approaches. Expert Systems with Applications, 241, 122686. https://doi.org/10.1016/j.eswa.2023.122686
      
      
        [9] Usmani, M., Memon, Z. A., Zulfiqar, A., &amp; Qureshi, R. (2024). Preptimize: Automation of time series data preprocessing and forecasting. Algorithms, 17(8), 332. https://doi.org/10.3390/a17080332
      
      
        [10] Lotysh, V., Gumeniuk, L., &amp; Humeniuk, P. (2023). Comparison of the effectiveness of time series analysis methods: SMA, WMA, EMA, EWMA, and Kalman filter for data analysis. Informatyka, automatyka, pomiary w gospodarce i ochronie środowiska, 13(3), 71-74. https://doi.org/10.35784/iapgos.3652
      
      
        [11] Andrasto, T., Ristanto, R. D., Sukamta, S., Aufa, A., Hafidz, C. M., Sandyawan, B., &amp; Alfarisi, A. F. (2023, June). Method EWMA (Exponentially Weighted Moving Average) as a filter to fine and remove noise on time series data. In IOP Conference Series: Earth and Environmental Science (Vol. 1203, No. 1, p. 012012). IOP Publishing. https://doi.org/10.1088/1755-1315/1203/1/012012
      
      
        [12] Curiël, R., Alsahag, A. M. M., &amp; Mohammadi Ziabari, S. S. (2025). Integrating Climate and Economic Predictors in Hybrid Prophet–(Q) LSTM Models for Sustainable National Energy Demand Forecasting: Evidence from The Netherlands. Sustainability, 17(19), 8687. https://doi.org/10.3390/su17198687
      
      
        [13] Zulqarnain, F. N. U., &amp; Hasan, Z. (2024). Artificial Intelligence Applications For Predicting Renewable-Energy Demand Under Climate Variability. American Journal of Scholarly Research and Innovation, 3(01), 84-116. https://doi.org/10.63125/sg0j6930
      
      
        [14] Al-Suod, M., Victor, B., Valerii, T., Olha, C., Galina, S., Zannon, M., &amp; Dmytro, Z. (2025). Forecasting energy consumption of a mining plant using artificial neural networks. IEEE Access, 13, 63237-63247. https://doi.org/10.1109/ACCESS.2025.3558445
      
      
        [15] Maharana, K., Mondal, S., &amp; Nemade, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1), 91-99. https://doi.org/10.1016/j.gltp.2022.04.020
      
      
        [16] Goceri, E. (2023). Medical image data augmentation: techniques, comparisons and interpretations. Artificial intelligence review, 56(11), 12561-12605. https://doi.org/10.1007/s10462-023-10453-z
      
      
        [17] Mazibuko, T., &amp; Akindeji, K. (2025). Hybrid forecasting for energy consumption in south Africa: LSTM and XGBoost approach. Energies, 18(16), 4285. https://doi.org/10.3390/en18164285
      
      
        [18] Noorunnahar, M., Chowdhury, A. H., &amp; Mila, F. A. (2023). A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh. PloS one, 18(3), e0283452. https://doi.org/10.1371/journal.pone.0283452
      
      
        [19] Bai, Y., &amp; Cai, C. X. (2024). Predicting VIX with adaptive machine learning. Quantitative Finance, 24(12), 1857-1873. https://doi.org/10.1080/14697688.2024.2439458
      
      
        [20] Zhuo, H., Wang, Y., Li, S., Ma, H., Wang, Y., Li, R., &amp; Liu, G. (2026). Enhancing Electricity Price Prediction Accuracy With an Attention Mechanism‐LSTM Hybrid Model. Engineering Reports, 8(3), e70657. https://doi.org/10.1002/eng2.70657
      
      
        [21] Zeng, C., Tian, Y., Zheng, G., &amp; Gao, Y. (2024). How much can time-related features enhance time series forecasting?. arXiv preprint arXiv:2412.01557. https://doi.org/10.48550/arXiv.2412.01557
      
      
        [22] Khashei, M., Ahmadi, M., &amp; Chahkoutahi, F. (2025). A mean weighted squared error-based neural classifier for intelligent pattern recognition in smart grids. International Journal of Electrical Power &amp; Energy Systems, 170, 110972. https://doi.org/10.1016/j.ijepes.2025.110972
      
      
        [23] Ao, X., Gong, Y., &amp; He, A. (2025). A review of time series prediction models based on deep learning. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3602791
      
      
        [24] Yan, L., Peng, J., Gao, D., Wu, Y., Liu, Y., Li, H., ... &amp; Huang, Z. (2022). A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery. Energy, 243, 123038. https://doi.org/10.1016/j.energy.2021.123038
      
      
        [25] Nong, Y. (2025). AI-Driven Predictive Cascade Failure Analysis Using Multi-Modal Environmental-Infrastructure Data Fusion Real-Time Prediction Framework for Critical Energy Infrastructure. Authorea Preprints. https://doi.org/10.36227/techrxiv.176288076.69687745/v2
      
      
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        [29] Wang, M., Chen, L., &amp; Chen, H. (2022). Multi-strategy learning boosted colony predation algorithm for photovoltaic model parameter identification. Sensors, 22(21), 8281. https://doi.org/10.3390/s22218281
      
      
        [30] Wang, Y., Wu, H., Dong, J., Liu, Y., Wang, C., Long, M., &amp; Wang, J. (2026). Deep time series models: A comprehensive survey and benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2026.3690845
      
      
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        [32] Zhang, Q. (2026). Research on Multimodal Prediction Model of Sports Event Ticket Revenue Based on Transformer. International Scientific Technical and Economic Research, 4(2), 124-147. https://doi.org/10.71451/ISTAER2618
      
      
        [33] Zhou, H. (2026). Research on the Fusion Model of DeepFM and XGBoost for Digital Consumer Behavior Prediction. International Scientific Technical and Economic Research, 4(2), 98-123. https://doi.org/10.71451/ISTAER2617
      
      
        [34] Qin, Y. (2026). Research on Long Sequence Learning Behavior Modeling Based on Transformer-XL. International Scientific Technical and Economic Research, 4(2), 1-20. https://doi.org/10.71451/ISTAER2613
      
      
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</p>
  </body>
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      <ref id="R1">
        <mixed-citation>[1] Alarcón, R. G., Alarcón, M. A., González, A. H., &amp; Ferramosca, A. (2025). Artificial Neural Networks for Energy Demand Prediction in an Economic MPC‐Based Energy Management System. International Journal of Robust and Nonlinear Control, 35(2), 642-658. https://doi.org/10.1002/rnc.7671</mixed-citation>
      </ref>
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        <mixed-citation>[2] Karpavicius, T., Balezentis, T., &amp; Streimikiene, D. (2025). Energy security indicators for sustainable energy development: Application to electricity sector in the context of state economic decisions. Sustainable Development, 33(1), 1381-1400. https://doi.org/10.1002/sd.3190</mixed-citation>
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