scholarly journals Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

2019 ◽  
Vol 9 (18) ◽  
pp. 3670 ◽  
Author(s):  
Natsheh ◽  
Samara
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Output Power ◽  
Maximum Output Power ◽  
Maximum Output ◽  
Atmospheric Conditions ◽  
Power Prediction ◽  
Different Types ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

Much work has been carried out for modeling the output power of photovoltaic panels. Using artificial neural networks (ANNS), one could efficiently model the output power of heterogeneous photovoltaic (HPV) panels. However, due to the existing different types of artificial neural network implementations, it has become hard to choose the best approach to use for a specific application. This raises the need for studies that develop models using the different neural networks types and compare the efficiency of these different types for that specific application. In this work, two neural network types, namely, the nonlinear autoregressive network with exogenous inputs (NARX) and the deep feed-forward (DFF) neural network, have been developed and compared for modeling the maximum output power of HPV panels. Both neural networks have four exogenous inputs and two outputs. Matlab/Simulink is used in evaluating the proposed two models under a variety of atmospheric conditions. A comprehensive evaluation, including a Diebold-Mariano (DM) test, is applied to verify the ability of the proposed networks. Moreover, the work further investigates the two developed neural networks using their actual implementation on a low-cost microcontroller. Both neural networks have performed very well; however, the NARX model performance is much better compared with DFF. Using the NARX network, a prediction of PV output power could be obtained, with half the execution time required to obtain the same prediction with the DFF neural network, and with accuracy of ±0.18 W.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals Identificación de figuras geométricas en un sistema de visión basándose en el entrenamiento de una red neuronal artificial convolucional utilizando Python

2019 ◽  
pp. 7-16
Author(s):  
Martha Isabel Aguilera-Hernández ◽  
Jorge Alan Velasco-Marín ◽  
Manuel Ortiz-Salazar ◽  
Jose Luis Ortiz-Simón
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Industry 4.0 ◽  
Vision System ◽  
Support Point ◽  
Process Systems ◽  
Geometric Figures ◽  
Different Types ◽  
Artificial Neural ◽  
Python Programming

The image processing projects through vision systems, are a great didactic support point in the mechatronics career, since they have wide application in the industry in the process lines primarily to perform assembly, inspection, selection and component placement. One of the methods used is to apply artificial neural networks for the identification of images and a factor to analyze is the evaluation of the learning capacities of these networks in the identification of geometric figures. In this article, the training of a convolutional artificial neural network using Python is presented. This type of work is focused on joining projects based on industry 4.0 that may contain link options with process systems based on these technologies. In this work, a vision system based on python programming was made and has its contribution in the libraries that were designed and can be linked to different types of applications within a manufacturing process.

scholarly journals Wind speed forecasting using neural networks

2019 ◽  
Vol 44 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Tyler Blanchard ◽  
Biswanath Samanta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
The United States ◽  
Weather Data ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

The prediction of wind speed is critical in the assessment of feasibility of a potential wind turbine site. This work presents a study on prediction of wind speed using artificial neural networks. Two variations of artificial neural networks, namely, nonlinear autoregressive neural network and nonlinear autoregressive neural network with exogenous inputs, were used to predict wind speed utilizing 1 year of hourly weather data from four locations around the United States to train, validate, and test these networks. This study optimized both neural network configurations and it demonstrated that both models were suitable for wind speed prediction. Both models outperformed persistence model (with a factor of about 2 to 10 in root mean square error ratio). Both artificial neural network models were implemented for single-step and multi-step-ahead prediction of wind speed for all four locations and results were compared. Nonlinear autoregressive neural network with exogenous inputs model gave better prediction performance than nonlinear autoregressive model and the difference was statistically significant.

scholarly journals Mengidentifikasi Tanaman Beracun pada Pola Daun dengan Jaringan Syaraf Tiruan Learning Vector Quantification

2019 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Muhammad Jurnalies Habibie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Poisonous Plants ◽  
Toxic Plants ◽  
Network Method ◽  
Different Types ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Technology nowadays is starting to go very fast, so that all people can use it. Toxic plants are very dangerous if consumed. Therefore to avoid undesirable events, an introduction to the community is needed to find out which plants are poisonous. Plants have many different types to recognize poisonous plants can be seen from the recognition of leaf patterns in these plants. For this reason, in order to determine the use of Learning Vector Quantification artificial neural networks. In this study, the use of input photos obtained from the camera. Photos will be processed later to extract the characteristics. Next, the process of pattern recognition can get the features in the photo. So that later it gets its characteristics. then the classification process uses the Learning Vector Quantification artificial neural network method. This research was conducted to be able to distinguish poisonous plants from those that are not. Which later the data is collected for grouping in accordance with the same data, so that information can be set about the plant.

scholarly journals Modeling of Harmonic Current in Electrical Grids with Photovoltaic Power Integration Using a Nonlinear Autoregressive with External Input Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4015
Author(s):  
Adán Alberto Jumilla-Corral ◽  
Héctor Enrique Campbell-Ramírez ◽  
Carlos Perez-Tello ◽  
Zulma Yadira Medrano-Hurtado ◽  
Pedro Mayorga-Ortiz ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
External Input ◽  
Electric Power Supply ◽  
Photovoltaic Power ◽  
Electrical Grids ◽  
The Neural Network ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

This research presents the modeling and prediction of the harmonic behavior of current in an electric power supply grid with the integration of photovoltaic power by inverters using artificial neural networks to determine if the use of the proposed neural network is capable of capturing the harmonic behavior of the photovoltaic energy integrated into the user's electrical grids. The methodology used was based on the use of recurrent artificial neural networks of the nonlinear autoregressive with external input type. Work data were obtained from experimental sources through the use of a test bench, measurement, acquisition, and monitoring equipment. The input–output parameters for the neural network were the current values in the inverter and the supply grid, respectively. The results showed that the neural network can capture the dynamics of the analyzed system. The generated model presented flexibility in data handling, allowing to represent and predict the behavior of the harmonic phenomenon. The obtained algorithm can be transferred to physical or virtual systems for the control or reduction of harmonic distortion.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

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