ARCHIVES
Year 2026 · Volume 5 · Issue 3
Original Article
A Heterogeneous Ensemble Framework Using Hybrid Deep Learning and XGBoost for Hourly Electrical Load Forecasting
J Uma Maheshwara Rao1
1 Department of Computer Science and Systems Engineering, Andhra University College of Engineering (A), Visakhapatnam, Andhra Pradesh, India.
Published Online: September-December 2026
Pages: 213-223
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503027
References
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4. K. Cho, B. van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, "Learning phrase representations using RNN encoder–decoder for statistical machine translation," in Proc. EMNLP, 2014, pp. 1724–1734, doi: 10.3115/v1/D14-1179.
5. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, "Attention is all you need," in Advances in Neural Information Processing Systems, 2017.
6. Keras/TensorFlow, "MultiHeadAttention layer," documentation, accessed 2026.
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8. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD '16), San Francisco, CA, USA, 2016, pp. 785–794, doi: 10.1145/2939672.2939785.
9. K. Hua, Z. Wang, F. Zuo, and Q. Wang, "Short-term electricity load forecasting method based on attention and time series forecasting," in Artificial Intelligence Technologies and Applications, 2024, doi: 10.3233/FAIA231386.
10. S. Salehimehr, B. Taheri, and M. Sedighizadeh, "Short-term load forecasting in smart grids using artificial intelligence methods: A survey,", 2022.
11. A. K. Hareendran, D. Verma C, V. Kumar P, D. Sharma, and V. C, "Electric load forecasting using ML for peak demand management in smart grids,"
12. M. A. A. Sarker, B. Shanmugam, S. Azam, and S. Thennadil, "Enhancing smart grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability," Intelligent Systems with Applications, vol. 23, p. 200422, Sep. 2024, doi: 10.1016/j.iswa.2024.200422.
13. "Daily load forecasting using explainable AI-driven method with integrated error correlation," IEEE Access, vol. 13, pp. 207496–207510, 2025, doi: 10.1109/ACCESS.2025.3640843.
14. F. Wang and Z. Nishtar, "Real-time load forecasting and adaptive control in smart grids using a hybrid neuro-fuzzy approach," Energies, vol. 17, no. 11, p. 2539, May 2024, doi: 10.3390/en17112539.
15. J. Roy, M. M. Alam, A. Chanda, and R. Das, "Smart grid optimization using AI-driven load forecasting and renewable energy integration,"
16. Y. Gou, C. Guo, and R. Qin, "Ultra-short-term power load forecasting based on the fusion of Seq2Seq BiLSTM and multi-head attention mechanism,"
17. D. H. Wolpert, "Stacked generalization," Neural Networks, vol. 5, no. 2, pp. 241–259, 1992, doi: 10.1016/S0893-6080(05)80023-1.
18. J. Foster and S. McLoone, "Stacked ensemble models for short-term electricity demand forecasting," Energy Power & Intelligent Control Research Cluster, Queen's University Belfast.
19. C. Min, Y. Wang, B. Zhang, X. Ma, and J. Cui, "A multi-source data power load forecasting method using attention mechanism-based parallel CNN-GRU."
20. R. Gao and P. N. Suganthan, "Random vector functional link neural network-based ensemble deep learning for short-term load forecasting,"
21. A. Muqtadir, B. Li, S. Chen, and S. N. Kazmi, "Nowcasting the next hour of residential load using boosting ensemble machines,"
22. E. Aguilar Madrid and N. Antonio, "Short-term electricity load forecasting with machine learning,"
2. Short-term electricity load forecasting by deep learning: A comprehensive survey, Engineering Applications of Artificial Intelligence, vol. 154, 110980, 2025, doi: 10.1016/j.engappai.2025.110980.
3. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.
4. K. Cho, B. van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, "Learning phrase representations using RNN encoder–decoder for statistical machine translation," in Proc. EMNLP, 2014, pp. 1724–1734, doi: 10.3115/v1/D14-1179.
5. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, "Attention is all you need," in Advances in Neural Information Processing Systems, 2017.
6. Keras/TensorFlow, "MultiHeadAttention layer," documentation, accessed 2026.
7. L. Breiman, "Stacked regressions," Machine Learning, vol. 24, pp. 49–64, 1996, doi: 10.1007/BF00117832.
8. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD '16), San Francisco, CA, USA, 2016, pp. 785–794, doi: 10.1145/2939672.2939785.
9. K. Hua, Z. Wang, F. Zuo, and Q. Wang, "Short-term electricity load forecasting method based on attention and time series forecasting," in Artificial Intelligence Technologies and Applications, 2024, doi: 10.3233/FAIA231386.
10. S. Salehimehr, B. Taheri, and M. Sedighizadeh, "Short-term load forecasting in smart grids using artificial intelligence methods: A survey,", 2022.
11. A. K. Hareendran, D. Verma C, V. Kumar P, D. Sharma, and V. C, "Electric load forecasting using ML for peak demand management in smart grids,"
12. M. A. A. Sarker, B. Shanmugam, S. Azam, and S. Thennadil, "Enhancing smart grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability," Intelligent Systems with Applications, vol. 23, p. 200422, Sep. 2024, doi: 10.1016/j.iswa.2024.200422.
13. "Daily load forecasting using explainable AI-driven method with integrated error correlation," IEEE Access, vol. 13, pp. 207496–207510, 2025, doi: 10.1109/ACCESS.2025.3640843.
14. F. Wang and Z. Nishtar, "Real-time load forecasting and adaptive control in smart grids using a hybrid neuro-fuzzy approach," Energies, vol. 17, no. 11, p. 2539, May 2024, doi: 10.3390/en17112539.
15. J. Roy, M. M. Alam, A. Chanda, and R. Das, "Smart grid optimization using AI-driven load forecasting and renewable energy integration,"
16. Y. Gou, C. Guo, and R. Qin, "Ultra-short-term power load forecasting based on the fusion of Seq2Seq BiLSTM and multi-head attention mechanism,"
17. D. H. Wolpert, "Stacked generalization," Neural Networks, vol. 5, no. 2, pp. 241–259, 1992, doi: 10.1016/S0893-6080(05)80023-1.
18. J. Foster and S. McLoone, "Stacked ensemble models for short-term electricity demand forecasting," Energy Power & Intelligent Control Research Cluster, Queen's University Belfast.
19. C. Min, Y. Wang, B. Zhang, X. Ma, and J. Cui, "A multi-source data power load forecasting method using attention mechanism-based parallel CNN-GRU."
20. R. Gao and P. N. Suganthan, "Random vector functional link neural network-based ensemble deep learning for short-term load forecasting,"
21. A. Muqtadir, B. Li, S. Chen, and S. N. Kazmi, "Nowcasting the next hour of residential load using boosting ensemble machines,"
22. E. Aguilar Madrid and N. Antonio, "Short-term electricity load forecasting with machine learning,"
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