Snow Loss Prediction for Photovoltaic Farms Using Computational Intelligence Techniques
| Autor: | Ana-Maria Cretu, Behzad Hashemi, Shamsodin Taheri |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Meteorology
Artificial neural network Computer science 020209 energy 020208 electrical & electronic engineering Photovoltaic system Computational intelligence 02 engineering and technology Condensed Matter Physics Snow 7. Clean energy Electronic Optical and Magnetic Materials Random forest Data modeling Support vector machine 13. Climate action 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Predictive modelling |
| Zdroj: | IEEE Journal of Photovoltaics. 10:1044-1052 |
| ISSN: | 2156-3403 2156-3381 |
| DOI: | 10.1109/jphotov.2020.2987158 |
| Popis: | With the recent widespread deployment of Photovoltaic (PV) panels in the northern snow-prone areas, performance analysis of these panels is getting much more importance. Partial or full reduction in energy yield due to snow accumulation on the surface of PV panels, which is referred to as snow loss, reduces their operational efficiency. Developing intelligent algorithms to accurately predict the future snow loss of PV farms is addressed in this article for the first time. The article proposes daily snow loss prediction models using machine learning algorithms solely based on meteorological data. The algorithms include regression trees, gradient boosted trees, random forest, feed-forward and recurrent artificial neural networks, and support vector machines. The prediction models are built based on the snow loss of a PV farm located in Ontario, Canada which is calculated using a 3-stage model and hourly data records over a 4-year period. The stages of the aforementioned model consist of: stage I: yield determination, stage II: power loss calculation, and stage III: snow loss extraction. The functionality of the proposed prediction models is validated over the historical data and the optimal hyperparameters are selected for each model to achieve the best results. Among all the models, gradient boosted trees obtained the minimum prediction error and thus the best performance. The results achieved prove the effectiveness of the proposed models for the prediction of daily snow loss of PV farms. |
| Databáze: | OpenAIRE |
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