Version 1
: Received: 31 August 2021 / Approved: 2 September 2021 / Online: 2 September 2021 (08:03:24 CEST)
How to cite:
Freitas, C.; De Carvalho, R.A. Artificial Neural Networks Applied to Power Management in Low Power Microprocessors. Preprints2021, 2021090033. https://doi.org/10.20944/preprints202109.0033.v1
Freitas, C.; De Carvalho, R.A. Artificial Neural Networks Applied to Power Management in Low Power Microprocessors. Preprints 2021, 2021090033. https://doi.org/10.20944/preprints202109.0033.v1
Freitas, C.; De Carvalho, R.A. Artificial Neural Networks Applied to Power Management in Low Power Microprocessors. Preprints2021, 2021090033. https://doi.org/10.20944/preprints202109.0033.v1
APA Style
Freitas, C., & De Carvalho, R.A. (2021). Artificial Neural Networks Applied to Power Management in Low Power Microprocessors. Preprints. https://doi.org/10.20944/preprints202109.0033.v1
Chicago/Turabian Style
Freitas, C. and Rogerio Atem De Carvalho. 2021 "Artificial Neural Networks Applied to Power Management in Low Power Microprocessors" Preprints. https://doi.org/10.20944/preprints202109.0033.v1
Abstract
Computer systems that operate in remote locations such as satellites, remote weather stations and autonomous robots are highly limited in the availability of energy for their operation. This work aims to employ artificial intelligence algorithms in energy management in order to obtain the maximum energy yield and the prediction of energy availability to the system. The work presents the main types of algorithms used in artificial intelligence and presents the creation of a prototype that will operate as a low power system powered by batteries and a small solar plate, the prototype will perform inferences on the LSTM neural network algorithm in order to predict the future availability of energy, consequently the management system will carry out the energy distribution in order to obtain the maximum operation of the prototype without total discharge of the batteries. So that the artificial intelligence system could be embedded in the prototype, the TensorFlow Lite framework was used, which allows the inference to be carried out in devices with low consumption and limited processing power.
Keywords
Artificial Intelligence; Low Power Systems; Power Management; LSTM
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
