Name
Abstract | ||
|---|---|---|
Fault diagnosis of photovoltaic (PV) arrays is an important task for improving the reliability and safety of the overall photovoltaic system. In the traditional fault diagnosis, most of the past researches focus on the I-V curve to determine the PV arrays state. However, most inverters as smart meters cannot obtain data in high frequency and high volume in order to calculate the I-V curve, and the information usually cannot be retrieved conveniently. In this paper, we evaluate various Recurrent Neural Network Models' from the recent advances in the deep learning field and their abilities in predicting PV arrays power generation. The results show the Encoder-Decoder model using Convolutional Neural Network (CNN) as the decoder and Temporal Convolutional Network (TCN) are more effective in predicting the PV arrays solar output production and can be used as the foundation for fault diagnosis and prediction. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICUMT48472.2019.8970993 | 2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) |
Keywords | Field | DocType |
Deep Learning,Time Series Prediction,Photovoltaic (PV) Power Forecasting | Time series,Convolutional neural network,Computer science,Performance ratio,Recurrent neural network,Electronic engineering,Artificial intelligence,Deep learning,Artificial neural network,Photovoltaic system,Electricity generation,Distributed computing | Conference |
ISSN | ISBN | Citations |
2157-0221 | 978-1-7281-5765-8 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chih-Feng Yen | 1 | 0 | 0.34 |
| He-Yen Hsieh | 2 | 0 | 0.34 |
| Kuan-Wu Su | 3 | 0 | 0.34 |
| Jenq-Shiou Leu | 4 | 238 | 40.64 |