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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Adaptive energy management strategy for solar energy harvesting IoT nodes by evolutionary fuzzy rules
Original language description
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'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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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