Estimation of Solar PV Plant output using Polynomial Regression and ANN Algorithm

The paper proposes the use of Artificial Neural Networks (ANN) and Polynomial Regression (PR) algorithms for estimating the output of a Solar Photovoltaic (PV) Plant. The study compares the performance of these two methods, using a dataset of weather parameters and PV Plant output. The results show...

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Vydané v:Journal of physics. Conference series Ročník 2601; číslo 1; s. 12017 - 12025
Hlavní autori: Kulkarni, Rohan, Kakade, Suhas, Mawande, Shamli, Sadakale, Ranjit
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bristol IOP Publishing 01.09.2023
Predmet:
Algorithms
Artificial neural networks
Estimation
Machine learning
Parameters
Photovoltaic cells
Physics
Polynomials
Regression models
ISSN:1742-6588, 1742-6596
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Popis
Shrnutí:The paper proposes the use of Artificial Neural Networks (ANN) and Polynomial Regression (PR) algorithms for estimating the output of a Solar Photovoltaic (PV) Plant. The study compares the performance of these two methods, using a dataset of weather parameters and PV Plant output. The results show that both the methods can effectively estimate the PV Plant output, with the ANN model having a slightly higher accuracy than the polynomial regression model as ANNs can learn the nonlinear relationship between input stochastic parameters and output in a better manner. The study highlights the potential of machine learning techniques for optimizing the performance of solar PV plants and improving their efficiency.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2601/1/012017