Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

Elsisi, Mahmoud; Tran, Minh-Quang; Mahmoud, Karar; Lehtonen, Matti; Darwish, Mohamed M. F.

doi:10.3390/s21041038

Cited by 137 publications

(75 citation statements)

References 38 publications

(45 reference statements)

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“…Further, Table 2 shows the lower and upper bounds of the decision variables of the model at two different operating condi-tions, (operating condition #1: 1/1 bar and 343.15 K) for both stacks (250 W and NedStack PS6) and (operating condition #2: 3/5 bar and 353.15 k) for 250 W stack. The simulation results have been compared to other literature works [18][19][20][21][22]. Moreover, four competitive methods; SSA [46], SCA [47], MFO [48], and PSO [16] have been implemented to test the effectiveness of COA.…”

Section: Resultsmentioning

confidence: 99%

“…A PEMFC is considered one of the most promising devices that convert chemical energy fuels into electrical energy based on electrochemical responses [15][16][17][18]. Specifically, the PEMFCs have numerous rewards, e.g., good electrical efficiency, little emission, and flexibility in fuel, that make them applicable to diverse applications [20][21][22][23][24]. For instance, they can apply in the combined tough issue owing to PEMFC being a compound multivariable powerfully coupled scheme.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm

et al. 2021

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In recent years, the penetration of fuel cells in distribution systems is significantly increased worldwide. The fuel cell is considered an electrochemical energy conversion component. It has the ability to convert chemical to electrical energies as well as heat. The proton exchange membrane (PEM) fuel cell uses hydrogen and oxygen as fuel. It is a low-temperature type that uses a noble metal catalyst, such as platinum, at reaction sites. The optimal modeling of PEM fuel cells improves the cell performance in different applications of the smart microgrid. Extracting the optimal parameters of the model can be achieved using an efficient optimization technique. In this line, this paper proposes a novel swarm-based algorithm called coyote optimization algorithm (COA) for finding the optimal parameter of PEM fuel cell as well as PEM stack. The sum of square deviation between measured voltages and the optimal estimated voltages obtained from the COA algorithm is minimized. Two practical PEM fuel cells including 250 W stack and Ned Stack PS6 are modeled to validate the capability of the proposed algorithm under different operating conditions. The effectiveness of the proposed COA is demonstrated through the comparison with four optimizers considering the same conditions. The final estimated results and statistical analysis show a significant accuracy of the proposed method. These results emphasize the ability of COA to estimate the parameters of the PEM fuel cell model more precisely.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm

et al. 2021

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“…Further, [24] showed many algorithms for fault detection algorithms are based on the comparison between measured output values and the reference modeled PV system outputs to determine the faults. It is established that metaheuristics, fuzzy logic, and artificial intelligence (AI) can provide improved performance in universal engineering applications [25][26][27][28][29][30][31][32]. Finally, References [33][34][35] described other approaches that use AI techniques, such as neural networks, fuzzy logic, and expert systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System

Bendary

Abdelaziz

Ismail

et al. 2021

Sensors

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In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 × 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems.

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“…Comparative MPPT studies by the common and AI techniques have been presented in [29,30], which have highlighted the features of employing the advanced algorithms. Further, artificial neural networks (ANNs) have been extensively utilized in different areas as rapid, precise, and robust tools due to their effective learning schemes [31][32][33]. Specifically, ANNs can simplify complex mathematical models by the dense connections among the neurons.…”

Section: Introductionmentioning

confidence: 99%

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic

Ali

Mahmoud

Lehtonen

et al. 2021

Sensors

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120

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This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.

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Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

Cited by 137 publications

References 38 publications

Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm

Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm

Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic

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