ArticleEnvironmental science and pollution research international2022
Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system.
Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Photovoltaic power prediction and fault diagnosis method based on LSTM and transformer.Scientific reports · 2026Article
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- Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm.Computational and mathematical methods in medicine · 2022Article
- Human Centered Decision-Making for COVID-19 Testing Center Location Selection: Tamil Nadu-A Case Study.Computational and mathematical methods in medicine · 2022Article
- COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques.Computational and mathematical methods in medicine · 2022Article
- A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection.Computational and mathematical methods in medicine · 2021Article
- An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques.BioMed research international · 2021Article
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4 authors.
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Abstract
The solar photovoltaic system is an emerging renewable energy resource. The performance of the solar photovoltaic system is predicted based on the historical experimental dataset. In this work, the real-time prediction models are developed for the output power prediction of the STPV system. The performance of the semitransparent photovoltaic system is predicted for the Kovilpatti region where the climatic condition is hot and humid. The short-term power is predicted for the hourly, daily, and weekly average are considered. The feature selected for the prediction of the output power of the STPV system comprises of the solar radiation, ambient temperature, and wind velocity of the Kovilpatti region. The result reveals that the output power prediction of the hourly, daily, and weekly power have the very high value of the correlation coefficient of R. The final model produced accurate forecasts, with a Root mean square (RMSE) of 0.25 in ELMAN and 0.30 in FFN and 0.426 in GRN. These features of the training algorithm indicate that the model is not dependent on the model's position or configuration in the simulation.
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