Prediction of environment-related operation and maintenance events in small hydropower plants

Selak, Luka (avtor), Škulj, Gašper (avtor), Kozjek, Dominik (avtor), Bračun, Drago (avtor)

Operation and maintenance (O&M) events resulting from environmental factors (e.g., precipitation, temperature, seasonality, and unexpected weather conditions) are among the primary sources of operating costs and downtime in run-of-river small hydropower plants (SHPs). This paper presents a data-driven methodology for predicting such long events using machine learning models trained on historical power production, weather radar, and forecast data. Case studies on two Slovenian SHPs with different structural designs and levels of automation demonstrate how environmental features—such as day of year, rain duration, cumulative amount of rain, and rolling precipitation sums—can be used to forecast long events or shutdowns. The proposed approach integrates probabilistic classification outputs with threshold-consistency smoothing to reduce noise and stabilize predictions. Several algorithms were tested—including Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting, and k-Nearest Neighbors (k-NN)—across varying feature combinations for O&M model development, with cross-validation ensuring robust evaluation. The models achieved an F1-score of up to 0.58 in SHP1 (k-NN), showing strong seasonality dependence, and up to 0.68 in SHP2 (Gradient Boosting). For SHP1, the best model (k-NN) correctly detected 36 long events, while 15 were misclassified as no events and 38 false alarms were produced. For SHP2, the best model (Gradient Boosting) correctly detected 69 long events, misclassified 23 as no events, and produced 42 false alarms. The findings highlight that probabilistic machine learning-based forecasting can effectively support predictive O&M planning, particularly for manually operated or service-operated SHPs.

• Jezik: Angleški jezik
• Ključne besede: small hydropower plants, environment related operation and maintenance, nonlinear predictive models
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 33 str.
• Številčenje: Vol. 7, no. 4, art. 163
• PID: 20.500.12556/RUL-177128
• UDK: 621.311.21
• ISSN pri članku: 2504-4990
• DOI: 10.3390/make7040163
• COBISS.SI-ID: 261667331
• Datum objave v RUL: 16.12.2025

Gradivo je del revije

• Naslov: Machine learning and knowledge extraction
• Založnik: MDPI
• ISSN: 2504-4990
• COBISS.SI-ID: 1537706179

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: male hidroelektrarne, okoljsko obratovanje, vzdrževanje, nelinearni napovedni modeli

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0270
• Naslov: Proizvodni sistemi, laserske tehnologije in spajanje materialov

• Financer: Drugi - Drug financer ali več financerjev
• Program financ.: Ministry of Higher Education, Science and Technology of Republic of Slovenia