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Efficient Implementation of a Self-sufficient Solar-Powered Real-Time Deep Learning-Based System

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

Abstract

This paper presents a self-sufficient solar-powered real-time Deep Learning (DL) based system that runs inference 100% on solar energy and which is composed of an Nvidia Jetson TX2 board and a dual-axis solar tracker based on the cast-shadow principle. In order to have a higher energy being generated by the solar tracker as well as a lower energy consumption by the real-time DL-based system, we have: a) updated our solar tracker’s panel with a higher number of polycrystalline photovoltaic (PV) cells and connected it to a chain of two inverters, one accumulator and one solar charge controller; b) implemented a motion detection method that triggers the inference process only when there is substantial movement in webcam frame. Experimental results show that our solar tracker generates sufficient and constant solar energy for all the 4 DL models (VGG-19, InceptionV3, ResNet-50 and MobileNetV2) that are running in real-time on the Nvidia Jetson TX2 platform and which requires more than 5 times less energy when compared to a laptop having a Nvidia GTX 1060 GPU, proving that real-time DL-based systems can be powered by solar trackers without the need of traditional power plugs or need to pay for electricity bills.

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Authors and Affiliations

  1. Advanced Computing Systems and Architectures (ACSA) Laboratory, Computers and Information Technology Department, “Politehnica” University of Timisoara, Timisoara, Romania

    Sorin Liviu Jurj, Raul Rotar, Flavius Opritoiu & Mircea Vladutiu

Authors
  1. Sorin Liviu Jurj
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  2. Raul Rotar
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  3. Flavius Opritoiu
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  4. Mircea Vladutiu
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Corresponding author

Correspondence to Sorin Liviu Jurj .

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Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Jurj, S.L., Rotar, R., Opritoiu, F., Vladutiu, M. (2020). Efficient Implementation of a Self-sufficient Solar-Powered Real-Time Deep Learning-Based System. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_7

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