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

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

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.

• Language: English
• Keywords: small hydropower plants, environment related operation and maintenance, nonlinear predictive models
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 33 str.
• Numbering: Vol. 7, no. 4, art. 163
• PID: 20.500.12556/RUL-177128
• UDC: 621.311.21
• ISSN on article: 2504-4990
• DOI: 10.3390/make7040163
• COBISS.SI-ID: 261667331
• Publication date in RUL: 16.12.2025

Record is a part of a journal

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

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: male hidroelektrarne, okoljsko obratovanje, vzdrževanje, nelinearni napovedni modeli

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0270
• Name: Proizvodni sistemi, laserske tehnologije in spajanje materialov

• Funder: Other - Other funder or multiple funders
• Funding programme: Ministry of Higher Education, Science and Technology of Republic of Slovenia