Applications of artificial neural networks and hybrid models for predicting CO2 flux from soil to atmosphere

Altıkat, Sefa; Gülbe, Alper; Küçükerdem, Hasan Kaan; Altikat, A.

doi:10.1007/s13762-020-02799-6

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…The learning algorithms used in the following are part of MATLAB toolbox of neural networks [18]. One of the indicators, that show the quality of the training operation, is the mean square error [19]- [21]. As shown in the following Figure 4, the mean square error gets the value 7.8488x10 -3 at 73 epochs, which shows that the training operation has worked well, which means that the MLP outputs will converge to the desired outputs perfectly in the three phases of training, testing and model validation [22]- [25].…”

Section: Training and Results Discussionmentioning

confidence: 99%

Integration of artificial neural networks for multi-source energy management in a smart grid

Jarmouni

Mouhsen

Lamhammedi

et al. 2021

IJPEDS

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<span lang="EN-US">Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.</span>

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Section: Training and Results Discussionmentioning

confidence: 99%

Integration of artificial neural networks for multi-source energy management in a smart grid

Jarmouni

Mouhsen

Lamhammedi

et al. 2021

IJPEDS

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show abstract

“…The Figure 6(b) shows that the mean square errors (MSE) corresponding to training, testing and validation converge to the same value of 0.01307x10 -4 . This shows that the training of the network is done successfully, and the MLP the output converges perfectly to the target output values [20]- [22].…”

Section: The Ann Techniquementioning

confidence: 92%

Integration of an optimized neural network in a photovoltaic system to improve maximum power point tracking efficiency

Jarmouni

Mouhsen

Lamhamdi

et al. 2022

IJEECS

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Due to the variability of weather conditions and equipment properties the maximum power point tracking (MPPT) performance is influenced. MPPT controllers are widely used to improve photovoltaic (PV) efficiency because MPPT can produce maximum power under various weather conditions. Among the most used techniques and representing a satisfactory efficiency are those based on artificial intelligence. Since the use of neural networks requires resources at the implementation level, the optimization of these systems is an important phase. This work represents an optimized system for tracking the maximum power point, the latter based on a multi-layer neural network. The optimized multi layer perceptron (MLP) will ensure a fast convergence to the maximum power point with a low oscillation compared to the classical method.

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“…On the output side there are also three vectors, two output vectors (S1 and S2) for controlling the charge and discharge of the battery and the third (S_G) output vector is used to control the connected and disconnected mode of the system with the public grid. To evaluate the performance of the various developed models, we used two statistical indicators: The correlation coefficient R and the mean square error MSE [20], [21]. To determine the most suited architecture of the network to be used, we varied the number of hidden layers, the number of neurons in a hidden layer, the transfer functions, the number of iterations and the learning algorithms [22].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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

Jarmouni¹,

Mouhsen²,

Lamhammedi³

et al. 2021

IJPEDS

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Nowadays, the combination of conventional and renewable energy sources such as solar energy is one of the most widespread solutions to surmount the challenge of the climate and energy crisis. In the presence of random behavior of photovoltaic systems and variable power demand by consumers, energy management is a real challenge. In this paper, we propose a new energy management technique based on artificial neural networks in a smart grid. This will ensure the continuous supply of electricity to the consumer in the presence of random operation in energy consumption and generation. The global system is modeled and simulated under the MATLAB/Simulink tool.

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Applications of artificial neural networks and hybrid models for predicting CO2 flux from soil to atmosphere

Cited by 3 publications

References 39 publications

Integration of artificial neural networks for multi-source energy management in a smart grid

Integration of artificial neural networks for multi-source energy management in a smart grid

Integration of an optimized neural network in a photovoltaic system to improve maximum power point tracking efficiency

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

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