Acta Scientific Computer Sciences

Research Article Volume 3 Issue 11

An Application of Artificial Intelligence Models for Predicting and Controlling Solar Cell Output Power

Deogratias Nurwaha*, Leonard Nduwayo and Salomon Mugisha

Institute of Applied Pedagogy, Department of Physics and Technology, University of Burundi, Bujumbura, Burundi

*Corresponding Author: Deogratias Nurwaha, Institute of Applied Pedagogy, Department of Physics and Technology, University of Burundi, Bujumbura, Burundi.

Received: September 14, 2021; Published:

Abstract

The study describes the application and comparison of five artificial intelligence methods for PV system output power prediction. General Regression Neural Network (GRNN), Radial Basis Function network (RBF), Group Method of Data Handling (GMDH) network, Multilayer Perceptron neural network (MLP) and Linear Regression (LR). Measured values of temperature (T°C) and irradiance E (kWh/m^2) were used as inputs (independent variables) and PV output power P (Kw) was used as output (dependent variable). Predictive performances have been evaluated using statistical metrics. Comparison of the results provided by the five models has been conducted and commented. It was observed that predictive accuracy depend of the nature of data set used and the optimization parameters of each model. Response surfaces that represent the combined impact of simultaneous variation in temperature and irradiance on PV output power have been illustrated. Curves that showed how close were validation predicted values and actual values have been plotted. Relationship between output power and the two parameters have been illustrated and it was found to be nonlinear. Importance of each ambient parameter contribution to the PV output power has been demonstrated.

Keywords: GMDH; GRNN; MLP; PV Output Power; RBF

Bibliography

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Citation

Citation: Deogratias Nurwaha., et al. “An Application of Artificial Intelligence Models for Predicting and Controlling Solar Cell Output Power". Acta Scientific Computer Sciences 3.11 (2021): 06-13..

Copyright

Copyright: © 2021 Deogratias Nurwaha., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate35%
Acceptance to publication20-30 days

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