Q-learning Algorithm for Energy Management in Solar Powered Embedded Monitoring Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241784" target="_blank" >RIV/61989100:27240/18:10241784 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/8477781" target="_blank" >https://ieeexplore.ieee.org/abstract/document/8477781</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CEC.2018.8477781" target="_blank" >10.1109/CEC.2018.8477781</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Q-learning Algorithm for Energy Management in Solar Powered Embedded Monitoring Systems

Popis výsledku v původním jazyce

Environmental changes have become a considerable issue over the past years. To be able to continuously monitor such variations in the environment, it is convenient to employ so-called Environmental Monitoring Systems (EMS) that can be deployed in remote places of interest, and that are capable of measuring multiple ambient characteristics. However, supplying EMS with power in a long term to prevent blackouts is challenging. This paper introduces an energy management method based on Reinforcement Learning algorithm, particularly Q-learning. The EMS described in this study needs to meet a set of strict requirements, e.g. low power consumption, high reliability, self-sustainability with respect to power supply, backup of data acquired through the measurements in case of an unexpected failure and many others. The energy is harvested using solar panels and stored in supercapacitors. In addition, the implementation of a complex algorithm is not suitable for such a system, considering the energy constraints. The solution to the above mentioned challenges is described in this study. The findings were implemented in a physical EMS device, and subsequently tested in field so as to acquire real-life data.

Název v anglickém jazyce

Q-learning Algorithm for Energy Management in Solar Powered Embedded Monitoring Systems

Popis výsledku anglicky

Environmental changes have become a considerable issue over the past years. To be able to continuously monitor such variations in the environment, it is convenient to employ so-called Environmental Monitoring Systems (EMS) that can be deployed in remote places of interest, and that are capable of measuring multiple ambient characteristics. However, supplying EMS with power in a long term to prevent blackouts is challenging. This paper introduces an energy management method based on Reinforcement Learning algorithm, particularly Q-learning. The EMS described in this study needs to meet a set of strict requirements, e.g. low power consumption, high reliability, self-sustainability with respect to power supply, backup of data acquired through the measurements in case of an unexpected failure and many others. The energy is harvested using solar panels and stored in supercapacitors. In addition, the implementation of a complex algorithm is not suitable for such a system, considering the energy constraints. The solution to the above mentioned challenges is described in this study. The findings were implemented in a physical EMS device, and subsequently tested in field so as to acquire real-life data.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

ISBN

978-1-5090-6017-7

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

7

Strana od-do

1068-1074

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Rio de Janeiro

Datum konání akce

8. 7. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000451175500138