Multi-agent system based on the fuzzy control and extreme learning machine for intelligent management in hybrid energy system

Bourakadi, Dounia El; Yahyaouy, Ali; Boumhidi, Jaouad

doi:10.1109/isacv.2017.8054922

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…In this section, we will introduce the results and discuss the numerical analyses. To evaluate the effectiveness of the suggested wind prediction R-ELM-GA model, we used a set of wind speeds from Tetouan City in Morocco [30]. The dataset for the modeling has been divided into training and testing sets.…”

Section: Resultsmentioning

confidence: 99%

“…As a consequence, the optimization procedure determined 𝐿 be optimum at 12. To enhance and measure the forecasting performance of the model, we conducted a comparison etude using the most used algorithm in wind energy forecasting, namely the R-ELM [34], the fundamental ELM [30], the BP [14], and the support vector machines (SVM) [35] algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…The main components of the ELM construction are the input layer, the output layer, and the hidden layer, connected by links called weights. The initial input weights are chosen at random, and the output weights are established using the inverse Moore-Penrose function [30]. ELM outperforms other machine learning techniques when the computational value is low.…”

Section: Methods 21 Extreme Learning Machinementioning

confidence: 99%

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Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

Mansoury,

El Bourakadi,

Yahyaouy

et al. 2024

Bulletin EEI

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Through the much defiance facing energy today, it has become necessary to rely on wind energy as a source of unlimited renewable energies. However, energy planning and regulation require wind capacity forecasting, because oscillations of wind speed drastically affect directly power generation. Therefore, several scenarios must be provided to allow for estimating uncertainties. To deal with this problem, this paper exploits the major advantages of the regularized extreme learning machine algorithm (R-ELM) and thus proposes a model for predicting the wind energy generated for the next hour based on the time series of wind speed. The R-ELM is combined with the genetic algorithm which is designed to optimize the most important hyperparameter which is the number of hidden neurons. Thus, the proposed model aims to forecast the average wind power per hour based on the wind speed of the previous hours. The results obtained showed that the proposed method is much better than those reported in the literature concerning the precision of the prediction and the time convergence.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methods 21 Extreme Learning Machinementioning

confidence: 99%

See 1 more Smart Citation

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

Mansoury,

El Bourakadi,

Yahyaouy

et al. 2024

Bulletin EEI

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“…The optimal policy is obtained by iterating the learning mechanism based on the reward from the environment or an established neural network to approximate the environment model by the critic-actor architecture [14], [15]. Hence in the multi-agent systems, the RL is widely applied [16], such as in traffic control [17], [18], mobile robots [19]- [21], resource management [22], [23].…”

Section: Related Workmentioning

confidence: 99%

Hidden Markov Random Field for Multi-Agent Optimal Decision in Top-Coal Caving

Yang

Lin

et al. 2020

IEEE Access

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Applying model-based learning for the optimal decision of the multi-agent system is not trivial due to the expensive price or even the impossibility of obtaining the ground truth for training the model of the complex environment. Such as learning the optimal action of hydraulic supports in the top-coal caving, the optimal action could not accessible as the ground truth of the corresponding state in the intricate processes. Regarding the latent ground truth as the hidden variable is an effective method in the hidden Markov model. This paper extends the hidden variable of ground truth to the multi-agent system and proposes the hidden Markov random field (HMRF) model with reinforcement learning for optimizing the action decision of the multi-agent. In the HMRF model, the input states and the output actions of the multi-agent are considered as an observable random field and a latent Markov random field, respectively. Based on the HMRF model, the optimal decision is inferred by the maximum posterior probability with the prior probability obtained by Q-learning. Meanwhile, the parameters of the HMRF model are estimated by the expectation maximum algorithm. In the experiment, the top-coal caving demonstrates the effectiveness of the proposed method that the recall of top-coal is improved prominently with a very small price of increasing the rock-rate. Furthermore, the proposed method is employed to deal with the predator-preys problem in the gym. The experiment result shows that the communication between agents by the HMRF increases the reward of the preys. INDEX TERMS Hidden Markov random field, optimal decision, multi-agent, top-coal caving.

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