Energy management system and supervision in a micro-grid  using artificial neural network technique

Jarmouni, Ezzitouni; Mouhsen, Ahmed; Lamhammedi, Mohammed; Ouldzira, Hicham

doi:10.11591/ijpeds.v12.i4.pp2570-2579

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…In ref. [127], the study examines an energy management technique based on an NN which ensured a continuous supply of electricity during periods of unreliable energy harvesting from an energy source which behaved erratically [127]. In ref.…”

Section: Algorithmsmentioning

confidence: 99%

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Kucova,

Prauzek,

Konecny

et al. 2023

CAAI Trans on Intel Tech

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Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro‐sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy. The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG‐powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi‐supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.

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Section: Algorithmsmentioning

confidence: 99%

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Kucova,

Prauzek,

Konecny

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…These interruptions can have a major impact on critical loads such as data centers, hospitals and industrial facilities, causing damage, data loss and even risks to personal safety. In order to solve these problems, it is crucial to use intelligent energy management techniques and to put in place energy storage systems and secondary energy sources to compensate for fluctuations [3], [4].…”

Section: Introductionmentioning

confidence: 99%

The implementation of an optimized neural network in a hybrid system for energy management

Jarmouni,

Mouhsen,

Lamhamdi

et al. 2024

IJPEDS

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In the face of increasing global energy demand and volatile energy prices, many countries are searching for solutions to ensure their energy independence. One of the most popular solutions is to incorporate renewable energy sources in their energy systems. While there are many advantages to integrating renewable energy sources, it is important to note that their intermittent operation can present challenges. Energy storage and smart grid management systems are key solutions to overcome these challenges and ensure sustainable, reliable use of renewable energy sources. In this article, we present an intelligent electrical energy management system for hybrid energy systems. This management system is based on a multi-layer neural network that has undergone an architecture optimization phase to improve the accuracy of real-time energy management and simplify its implementation. The management model that was built demonstrated highly good performance across a range of test circumstances.

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“…However, researchers and industry professionals are now exploring innovative approaches to enhance the efficiency of electric vehicles and to address challenges related to power management, energy efficiency, and the unpredictable nature of operating conditions. One potentially promising strategy is the use of artificial intelligence for controlling of these converters, which has the potential to provide significant enhancements in terms of overall both effectiveness and dependability [6].…”

Section: Introductionmentioning

confidence: 99%

Comparing fuzzy logic and backstepping control for a buck boost converter in electric vehicles

Ennajih,

Allali,

Jarmouni

et al. 2024

IJPEDS

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Significant advances have been made in the control of DC/DC converters, owing to the effective combination of linear and nonlinear approaches. The above-mentioned approaches have greatly improved the efficiency as well as stability of the converter, even when faced with changing conditions. Nevertheless, the emergence of artificial intelligence has introduced new perspectives in the domain. The objective of the article is to examine two separate methodologies for controlling a boost-buck converter: using a nonlinear approach, especially backstepping, and utilizing artificial intelligence through fuzzy logic. The main aim of this work is to illustrate the inherent stability and robustness of fuzzy logic controllers compared to backstepping control in managing effectively various variations and challenges encountered in converter control.

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Energy management system and supervision in a micro-grid using artificial neural network technique

Cited by 6 publications

References 20 publications

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

The implementation of an optimized neural network in a hybrid system for energy management

Comparing fuzzy logic and backstepping control for a buck boost converter in electric vehicles

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