| Autor: |
Lars Maaløe, Ole Winther, Sergiu Spataru, Dezso Sera |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Energies, Vol 13, Iss 3, p 584 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
1996-1073 |
| DOI: |
10.3390/en13030584 |
| Popis: |
With the rapid increase in photovoltaic energy production, there is a need for smart condition monitoring systems ensuring maximum throughput. Complex methods such as drone inspections are costly and labor intensive; hence, condition monitoring by utilizing sensor data is attractive. In order to recognize meaningful patterns from the sensor data, there is a need for expressive machine learning models. However, supervised machine learning, e.g., regression models, suffer from the cumbersome process of annotating data. By utilizing a recent state-of-the-art semi-supervised machine learning based on probabilistic modeling, we were able to perform condition monitoring in a photovoltaic system with high accuracy and only a small fraction of annotated data. The modeling approach utilizes all the unsupervised data by jointly learning a low-dimensional feature representation and a classification model in an end-to-end fashion. By analysis of the feature representation, new internal condition monitoring states can be detected, proving a practical way of updating the model for better monitoring. We present (i) an analysis that compares the proposed model to corresponding purely supervised approaches, (ii) a study on the semi-supervised capabilities of the model, and (iii) an experiment in which we simulated a real-life condition monitoring system. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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