Predictive modeling and anomaly detection in solar PV inverters using machine learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F25%3A43910136" target="_blank" >RIV/60076658:12510/25:43910136 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2590123025043920" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590123025043920</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2025.108348" target="_blank" >10.1016/j.rineng.2025.108348</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predictive modeling and anomaly detection in solar PV inverters using machine learning

Popis výsledku v původním jazyce

The operational stability of photovoltaic (PV) systems is critical to the success of distributed renewable energy integration. This study presents a machine learning-driven framework for performance modeling, anomaly detection, and classification of inverter output in a grid-connected PV installation. Using high-resolution data collected from 30 kW and 40 kW inverters over one month, we applied supervised learning techniques to predict active power output, categorize production levels, and detect deviations from expected behavior. A Random Forest Regressor achieved high fidelity in power prediction (R2 = 0.995, MAE = 0.12 kW), while classification models categorized output levels with 100 % accuracy under static conditions. Anomaly detection using Z-score analysis identified significant outliers, particularly during high-production intervals. However, one-hour-ahead classification revealed substantial drops in predictive performance (accuracy = 36.4 %), highlighting the inherent difficulty of forecasting under variable environmental conditions. Feature importance analysis underscored the dominant role of inverter input power and phase currents in prediction accuracy. These findings demonstrate the utility of interpretable, data-driven models for real-time diagnostics and forecasting in PV systems and lay the groundwork for scalable smart monitoring infrastructures. Unlike prior studies that rely on meteorological inputs, this work uses only inverter and grid-side electrical measurements, demonstrating that interpretable models can provide actionable insights even in data-limited scenarios. The proposed framework offers a practical foundation for early-warning diagnostics and intelligent maintenance scheduling, enabling more resilient PV operations. Its integration of interpretable machine learning with real-time monitoring distinguishes it from conventional rule-based supervision methods.

Název v anglickém jazyce

Predictive modeling and anomaly detection in solar PV inverters using machine learning

Popis výsledku anglicky

The operational stability of photovoltaic (PV) systems is critical to the success of distributed renewable energy integration. This study presents a machine learning-driven framework for performance modeling, anomaly detection, and classification of inverter output in a grid-connected PV installation. Using high-resolution data collected from 30 kW and 40 kW inverters over one month, we applied supervised learning techniques to predict active power output, categorize production levels, and detect deviations from expected behavior. A Random Forest Regressor achieved high fidelity in power prediction (R2 = 0.995, MAE = 0.12 kW), while classification models categorized output levels with 100 % accuracy under static conditions. Anomaly detection using Z-score analysis identified significant outliers, particularly during high-production intervals. However, one-hour-ahead classification revealed substantial drops in predictive performance (accuracy = 36.4 %), highlighting the inherent difficulty of forecasting under variable environmental conditions. Feature importance analysis underscored the dominant role of inverter input power and phase currents in prediction accuracy. These findings demonstrate the utility of interpretable, data-driven models for real-time diagnostics and forecasting in PV systems and lay the groundwork for scalable smart monitoring infrastructures. Unlike prior studies that rely on meteorological inputs, this work uses only inverter and grid-side electrical measurements, demonstrating that interpretable models can provide actionable insights even in data-limited scenarios. The proposed framework offers a practical foundation for early-warning diagnostics and intelligent maintenance scheduling, enabling more resilient PV operations. Its integration of interpretable machine learning with real-time monitoring distinguishes it from conventional rule-based supervision methods.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Svazek periodika

28

Číslo periodika v rámci svazku

December 2025

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

001633402000009

EID výsledku v databázi Scopus

2-s2.0-105023579529