A Machine Learning-Based Early Warning System for Electricity Outage Due to Extreme Weather
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Dec 31, 2025
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Abstract
Electricity is a critical resource that supports various sectors in Indonesia, especially during extreme weather. Outages have become serious for operational risks during extreme weather. This study proposes a machine learning-based early warning system to predict electricity outages caused by extreme weather. Historical weather and outage data were combined using spatial alignment. Key innovation of this study involved geospatial feature enrichment via HDBSCAN, Yeo-Johnson transformation, robust scaling, and class resampling using SMOTE, ADASYN, and SMOTE-ENN. Four ensemble classification models (Random Forest, XGBoost, AdaBoost, and LightGBM) were evaluated. LightGBM with SMOTE yielded the highest recall (0.99) and the fewest false negatives. These findings suggest a solution for a proactive early warning system risk mitigation in electricity under extreme weather conditions.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Putra, H. S. M., Alit Kesatria Mendala, Intan Jelita Saragih, Neng Ayu Herawati, Ayu Purwarianti, & Nugraha Priya Utama. (2025). A Machine Learning-Based Early Warning System for Electricity Outage Due to Extreme Weather. Jurnal E-Komtek (Elektro-Komputer-Teknik), 9(2), 360-376. https://doi.org/10.37339/e-komtek.v9i1.2484
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[3] A. M. Brockway and L. N. Dunn, “Weathering adaptation: Grid infrastructure planning in a changing climate,” Jan. 01, 2020, Elsevier B.V. doi: 10.1016/j.crm.2020.100256.
[4] K. Alexakis, A. Michalitsi-Psarrou, D. Papaleloudis, P. Kokkinakos, and D. Askounis, “Proactive Alerting Solution: An LSTM Model for Power Outage Forecasting in Chalki, Greece,” in Proceedings of the 30th ICE IEEE/ITMC Conference on Engineering, Technology, and Innovation: Digital Transformation on Engineering, Technology and Innovation, ICE 2024, Institute of Electrical and Electronics Engineers Inc., 2024. doi: 10.1109/ICE/ITMC61926.2024.10794288.
[5] P. Chang, B. Tian, G. Li, Y. Sun, and H. Gao, “Prediction of Power Grid Line Faults Under Cold Wave Weather Based on Hybrid Model of XGBoost and LSTM,” in 2024 4th International Conference on Intelligent Power and Systems, ICIPS 2024, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 400–406. doi: 10.1109/ICIPS64173.2024.10900043.
[6] X. Wang, W. Liu, F. Ding, Y. Yao, and J. Zhao, “Preventive Power Outage Estimation Based on A Novel Scenario Clustering Strategy,” in IEEE Power and Energy Society General Meeting, IEEE Computer Society, 2024. doi: 10.1109/PESGM51994.2024.10760283.
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[9] N. Anggraini, S. J. Putra, L. K. Wardhani, F. D. U. Arif, N. Hakiem, and I. M. Shofi, “A Comparative Analysis of Random Forest, XGBoost, and LightGBM Algorithms for Emotion Classification in Reddit Comments,” JURNAL TEKNIK INFORMATIKA, vol. 17, no. 1, pp. 88–97, May 2024, doi: 10.15408/jti.v17i1.38651.
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[20] X. Jiang and Z. Ge, “Data Augmentation Classifier for Imbalanced Fault Classification,” IEEE Transactions on Automation Science and Engineering, vol. 18, no. 3, pp. 1206–1217, Jul. 2021, doi: 10.1109/TASE.2020.2998467.
[21] K. Umam Syaliman, “http://infor.seaninstitute.org/index.php/infokum/index INFOKUM is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License (CC BY-NC 4.0) ENHANCE THE ACCURACY OF K-NEAREST NEIGHBOR (K-NN) FOR UNBALANCED CLASS DATA USING SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE) AND GAIN RATIO (GR),” JURNAL INFOKUM, vol. 10, no. 1, 2021, [Online]. Available: http://infor.seaninstitute.org/index.php/infokum/index
[22] D. Salifu, L. Chepkemoi, E. A. Ibrahim, K. Nkoba, and H. E. Z. Tonnang, “Data Augmentation and Machine Learning algorithms for multi-class imbalanced morphometrics data of stingless bees,” Heliyon, vol. 11, no. 3, p. e42214, 2025, doi: https://doi.org/10.1016/j.heliyon.2025.e42214.
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