- B. M. Atsafack, D. Juma, G. Rushingabigwi, and C. Kabiri, “LSTM Autoencoder-Based Real-Time Anomaly Detection for Univariate Hydraulic Turbine Time-Series Data,” IEEE Sensors Journal, Early Access, 2026.
- Blondelle Melina Atsafack, Charles Kabiri, Gerard Rushingabigwi, A multi-model predictive framework for unsupervised anomaly detection in univariate time series data from hydraulic turbine units, Energy Reports, Volume 14, 2025, Pages 4701-4709, ISSN 2352-4847.
- B. M. Atsafack, C. Kabiri and G. Rushingabigwi, “Predictive Maintenance for Hydraulic Turbine Unit: A Comparative Deep Learning Approach Using Internet of Things Data in Real-Time,” in IEEE Access, vol. 13, pp. 158340-158352, 2025.
- A. B. Melina, F. Nzanywayingoma, C. Kabiri and G. Rushingabigwi, “Remote Monitoring of Micro-Hydropower plants using Python,” 2024 International Conference on Green Energy, Computing and Sustainable Technology (GECOST), Miri Sarawak, Malaysia, 2024, pp. 442-447.
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Atsafack Lab’s predictive models transformed our hydropower monitoring—accurate and timely insights.
The real-time anomaly detection helped us prevent costly downtime, truly a game changer in energy management.