Abstract
The main objective of this paper is to present a comparison between two models for estimation of a photovoltaic system’s module temperature (T\(_{mod}\)) using Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems. Both estimations use measurements of common operation variables: current, voltage and duty cycle (d) from a power converter of the photovoltaic system as input variables and T\(_{mod}\) as a desired output. The models used the same database for the training process, different training strategies were evaluated with the objective to find which model has the best estimation with respect to the T\(_{mod}\). Subsequently, the output results from these architectures are validated via the Root Mean Squared Error, Mean Absolute Percentage Error and correlation coefficient. Results show that the Artificial Neural Network model in comparison with Adaptive Neuro Fuzzy Inference System model provides a better estimation of T\(_{mod}\) with \(R = 0.8167\). Developed models may have an application with smart sensors on cooling systems for photovoltaic modules with the objective of improving their operation efficiency.
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Dzib, J.T. et al. (2016). Photovoltaic Module Temperature Estimation: A Comparison Between Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems Models. In: Martin-Gonzalez, A., Uc-Cetina, V. (eds) Intelligent Computing Systems. ISICS 2016. Communications in Computer and Information Science, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-319-30447-2_4
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