Hardware implementation of an artificial neural network model to predict the energy production of a photovoltaic system

Baptista, Daro; Rodrigues, Sandy; Travieso-Gonzlez, Carlos; Morgado‐Dias, Fernando

doi:10.1016/j.micpro.2016.11.003

Cited by 21 publications

(13 citation statements)

References 7 publications

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“…Figure 2 [35] shows the configuration of the multilayer perceptron with back propagation network. Due to the current computer technology, ANN has been widely applied in various engineering fields including the PV field [36][37][38][39].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

Nor

Salimin

Abdullah

et al. 2020

IJPEDS

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<span>Artiﬁcial Neural Network (ANN) techniques are becoming useful in the current era due to the vast development of the current computer technologies. ANN has been used in various fields especially in the field of science and technology. One of the advantage that makes ANN so interesting is the ANN’s ability to learn the input and output relationship even though the relationship is non-linear. In addition, ANN is also useful for modelling, optimization, prediction, forecasting, and controlling systems. The main objective of this paper is to present a review of the ANN techniques for sizing a stand-alone photovoltaic (PV) system. The review in this paper shows the potential of ANN as a design tool for a stand-alone PV. In addition, ANN is very useful to improve the sizing process of the stand-alone PV system. The sizing process is of paramount importance to a stand-alone PV system in order to make sure the system can generate ample electrical energy to supply the load demand.</span>

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Section: Artificial Neural Networkmentioning

confidence: 99%

Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

Nor

Salimin

Abdullah

et al. 2020

IJPEDS

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“…Many applications adopted ANN for classification, control and calibration tasks in biomedical instrumentation [11], [12], control systems [13], [14], non-conventional energy production [15], [16], etc. However, the efficient implementation and real-time execution of ANN models on embedded platforms are still a challenging problem because it involves plenty of nonlinear activation functions, numerous amounts of additions and multiplications.…”

Section: Related Workmentioning

confidence: 99%

“…The activity classification is done by the 3-axis accelerometer data located on the soldier waist. As this work focuses on the hardware design of the MLP classifier, we used the available accelerometer data set for the evaluation of MLP classifier hardware implementation [16]. A sensor node has been simulated in LabVIEW using this dataset.…”

Section: Hardware Implementation and Testingmentioning

confidence: 99%

Efficient FPGA Implementation of Multilayer Perceptron for Real-Time Human Activity Classification

et al. 2019

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The smartphone-based human activity recognition (HAR) systems are not capable to deliver high-end performance for challenging applications. We propose a dedicated hardware-based HAR system for smart military wearables, which uses a multilayer perceptron (MLP) algorithm to perform activity classification. To achieve the flexible and efficient hardware design, the inherent MLP architecture with parallel computation is implemented on FPGA. The system performance has been evaluated using the UCI human activity dataset with 7767 feature samples of 20 subjects. The three combinations of a dataset are trained, validated, and tested on ten different MLP models with distinct topologies. The MLP design with the 7-6-5 topology is finalized from the classification accuracy and cross entropy performance. The five versions of the final MLP design (7-6-5) with different data precision are implemented on FPGA. The analysis shows that the MLP designed with 16-bit fixed-point data precision is the most efficient MLP implementation in the context of classification accuracy, resource utilization, and power consumption. The proposed MLP design requires only 270 ns for classification and consumes 120 mW of power. The recognition accuracy and hardware results performance achieved are better than many of the recently reported works. INDEX TERMS Human activity recognition, multilayer perceptron, smart military wearables, classifier hardware implementation, field programmable gate array.

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“…Baptista et al. [26] , also used FPGA for implementing ANNs to predict the energy production of a photovoltaic system. The results show that the PV panel can be accurately modeled based on data from a nearby meteorological installation and the hardware implementation produces precise results.…”

Section: Introductionmentioning

confidence: 99%

Modeling the output power of heterogeneous photovoltaic panels based on artificial neural networks using low cost microcontrollers

Samara

Natsheh

2018

Heliyon

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Many implementations of artificial neural networks have been reported in scientific papers. However, few of these implementations allow the direct use of off-line trained networks. Moreover, no implementation reported the use of relatively small network adequate to run on low cost microcontroller. Hence, this work, which presents a small artificial neural network, which models the output power of heterogeneous photovoltaic panel. In addition, the work discuss the hardware implementation that allows such network to run on low cost microcontroller. The hardware implementation has the ability to model heterogeneous photovoltaic panel's output power with very high accuracy and fast response time. Feedforward back propagation has been used because of its high resolution and accurate activation function. Real-time measured parameters can be used as inputs for the developed system. The resulting hardware data is tested with data from real photovoltaic panels; to confirm that it can efficiently implement the models prepared off-line with Matlab. The comparison revealed the robustness of the proposed heterogeneous photovoltaic model system at different conditions. The proposed heterogeneous photovoltaic model system offer a proper and efficient tool that can be used in monitoring photovoltaic panels, such as the ones used in smart-house applications.

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Hardware implementation of an artificial neural network model to predict the energy production of a photovoltaic system

Cited by 21 publications

References 7 publications

Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

Efficient FPGA Implementation of Multilayer Perceptron for Real-Time Human Activity Classification

Modeling the output power of heterogeneous photovoltaic panels based on artificial neural networks using low cost microcontrollers

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