Abstract
This paper presents a comparative analysis of Levenberg-Marquardt (LM) and Bayesian Regularization (BR) backpropagation algorithms in development of different Artificial Neural Networks (ANNs) to estimate the output power of a Photovoltaic (PV) module. The proposed ANNs undergo training, validation and testing phases on 10000+ combinations of data including the real-time measurements of irradiance level (W/m2) and PV output power (W) as well as the calculations of the Sun’s position in the sky and the PV module surface temperature (°C). The overall performance of the LM and the BR algorithms are analyzed during the development phases of the ANNs, and also the results of implementation of each ANN in different time intervals with different input types are compared. The comparative study presents the trade-offs of utilizing LM and BR algorithms in order to develop the best ANN architecture for PV output power estimation.
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Notes
- 1.
The noisy data is filtered out in order to maintain reasonable and robust (MAPE) values.
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Jazayeri, K., Jazayeri, M., Uysal, S. (2016). Comparative Analysis of Levenberg-Marquardt and Bayesian Regularization Backpropagation Algorithms in Photovoltaic Power Estimation Using Artificial Neural Network. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_7
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