Article ; Online: Estimation of Photovoltaic Module Performance with L-Shaped Aluminum Fins Using Weather Data
Journal of Ecological Engineering, Vol 25, Iss 1, Pp 336-
2024 Volume 344
Abstract: PV power prediction is vital for efficient and effective solar energy utilization within the energy ecosystem. It enables grid stability, cost savings, and the seamless integration of solar power into the broader energy infrastructure. In this work, ... ...
Abstract | PV power prediction is vital for efficient and effective solar energy utilization within the energy ecosystem. It enables grid stability, cost savings, and the seamless integration of solar power into the broader energy infrastructure. In this work, previously obtained data on the estimation of the power produced by a PV, which is cooled by L-shaped aluminum fins attached to the backside of the PV at different spacings, is used to predict the power produced by the PV. This is achieved by employing both neural network models and multiple linear regression (MLR) techniques to assess the correlation between power generated by PV with L-shaped aluminum fins and its input variables. Two distinct approaches were employed for this purpose. The first approach involved the conventional MLR model, while the second utilized a neural network, specifically the Multilayer Perceptron (MLP) model. The estimated outcomes were subsequently compared against the previously measured data. The MLR technique showed a great ability to identify the relationship between input and output variables, it was noted. The statistical error study provided evidence of data mining's acceptable accuracy when using the MLP model. Conversely, the results indicated that the Multilayer Perceptron Network (MLP) model exhibited the least ability to estimate the power generated by PV with L-shaped aluminum Fins. |
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Keywords | artificial neural network ; pv ; multiple linear regression ; l-shaped aluminum fins ; Environmental technology. Sanitary engineering ; TD1-1066 ; Environmental sciences ; GE1-350 |
Subject code | 310 |
Language | English |
Publishing date | 2024-01-01T00:00:00Z |
Publisher | Polish Society of Ecological Engineering (PTIE) |
Document type | Article ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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