Adaptive energy management strategy for solar energy harvesting IoT nodes by evolutionary fuzzy rules

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255025" target="_blank" >RIV/61989100:27240/24:10255025 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2542660524001380" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2542660524001380</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.iot.2024.101197" target="_blank" >10.1016/j.iot.2024.101197</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive energy management strategy for solar energy harvesting IoT nodes by evolutionary fuzzy rules

Popis výsledku v původním jazyce

This study explores the integration of genetic programming (GP) and fuzzy logic to enhance control strategies for Internet of Things (IoT) nodes across varied locations. It is introduced a novel methodology for designing a fuzzy -based energy management controller that autonomously determines the most suitable controller structure and inputs. This approach is evaluated using a solar harvesting IoT model that leverages historical solar irradiance data, highlighting the methodology&apos;s potential for diverse geographical applications and compatibility with low -performance microcontrollers. The findings demonstrate that the integration of GP with designed fitness function enables the dynamic learning and adaptation of control strategies, optimizing system behavior based on historical data. The experimental model showcases an ability to efficiently use historical datasets to derive optimal control strategies, with the fitness metric indicating consistent improvement throughout the learning phase. The results indicate that useful control strategies learned at a certain location may outperform a locally -trained control strategies and can be successfully re -applied in other locations.

Název v anglickém jazyce

Adaptive energy management strategy for solar energy harvesting IoT nodes by evolutionary fuzzy rules

Popis výsledku anglicky

This study explores the integration of genetic programming (GP) and fuzzy logic to enhance control strategies for Internet of Things (IoT) nodes across varied locations. It is introduced a novel methodology for designing a fuzzy -based energy management controller that autonomously determines the most suitable controller structure and inputs. This approach is evaluated using a solar harvesting IoT model that leverages historical solar irradiance data, highlighting the methodology&apos;s potential for diverse geographical applications and compatibility with low -performance microcontrollers. The findings demonstrate that the integration of GP with designed fitness function enables the dynamic learning and adaptation of control strategies, optimizing system behavior based on historical data. The experimental model showcases an ability to efficiently use historical datasets to derive optimal control strategies, with the fitness metric indicating consistent improvement throughout the learning phase. The results indicate that useful control strategies learned at a certain location may outperform a locally -trained control strategies and can be successfully re -applied in other locations.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Internet of Things

ISSN

2543-1536

e-ISSN

2542-6605

Svazek periodika

26

Číslo periodika v rámci svazku

July 2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

—

Kód UT WoS článku

001237074400001

EID výsledku v databázi Scopus

2-s2.0-85191457437