A new approach for solar tracking, based on deep learning techniques, is being studied and tested using Tensorflow, an open source machine learning framework. Tensorflow makes the implementation more flexible and increases the development capabilities. Tensorflow facilitates the neural network implementation on several kinds of devices (embedded and mobile devices, mini computers, etc.). Furthermore, Tensorflow supports different types of neural networks which can be tuned and retrained for particular purposes. The presented results are promising, since the retrained networks correctly identify the Sun and the target, with this information the system can be controlled to properly track the Sun’s apparent trajectory without further information.
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Machine learning for solar trackers
Semantic Scholar · Computer Science · 2019
Abstract
A new approach for solar tracking, based on deep learning techniques, is being studied and tested using Tensorflow, an open source machine learning framework. Tensorflow makes the implementation more flexible and increases the development capabilities. Tensorflow facilitates the neural network implementation on several kinds of devices (embedded and mobile devices, mini computers, etc.). Furthermore, Tensorflow supports different types of neural networks which can be tuned and retrained for particular purposes. The presented results are promising, since the retrained networks correctly identify the Sun and the target, with this information the system can be controlled to properly track the Sun’s apparent trajectory without further information.