Abstract
The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don’t satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.
Highlights
The operation and management of the electrical system is becoming increasingly complex with the integration of renewable sources
This paper proposes a Nonlinear Autoregressive Exogenous (NARX) neural network model for the direct solar radiation prediction on
This paper proposes a NARX neural network model for the direct solar radiation prediction on a horizontal surface
Summary
The operation and management of the electrical system is becoming increasingly complex with the integration of renewable sources. In [5], the authors don’t classify the input data, but rather they train four neural networks with four combinations of input features, with the aim of considering the influence of several meteorological parameters on the prediction results They conclude that the best performance is obtained when using the following inputs: the year day, the mean daily solar radiation at the top of atmosphere, the maximum sunshine hours, the mean daily air temperature, the mean daily relative humidity and the wind speed. In [8], the authors propose a diagonal recurrent wavelet neural network, in order to estimate the hourly solar radiation of the day It is a model founded on the combination of wavelet neural network with recurrent ANN and fuzzy technology, where the wavelet basis is implemented as activation function for the neurons and the input vector of the neural network contains defuzzificated data of the nebulosity.
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