Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks

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.

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Long-term effects of outdoor air pollution on mortality and morbidity–prediction using nonlinear autoregressive and artificial neural networks models

Atmospheric Pollution Research ◽

10.1016/j.apr.2020.10.007 ◽

2020 ◽

Author(s):

Davood Namdar Khojasteh ◽

Gholamreza Goudarzi ◽

Ruhollah Taghizadeh-Mehrjardi ◽

Akwasi Bonsu Asumadu-Sakyi ◽

Masoud Fehresti-Sani

Keyword(s):

Neural Networks ◽

Air Pollution ◽

Artificial Neural Networks ◽

Outdoor Air ◽

Long Term Effects ◽

Mortality And Morbidity ◽

Outdoor Air Pollution ◽

Artificial Neural ◽

Nonlinear Autoregressive

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Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs

Energies ◽

10.3390/en10010040 ◽

2017 ◽

Vol 10 (1) ◽

pp. 40 ◽

Cited By ~ 34

Author(s):

Jaime Buitrago ◽

Shihab Asfour

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Short Term ◽

Artificial Neural ◽

Short Term Forecasting ◽

Electric Loads ◽

Nonlinear Autoregressive

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

Energies ◽

10.3390/en14134015 ◽

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.

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Pipeline Inspection Gauge’s Velocity Simulation Based on Pressure Differential Using Artificial Neural Networks

Sensors ◽

10.3390/s18093072 ◽

2018 ◽

Vol 18 (9) ◽

pp. 3072 ◽

Cited By ~ 2

Author(s):

Renan de Araújo ◽

Victor de Freitas ◽

Gustavo de Lima ◽

Andrés Salazar ◽

Adrião Neto ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Petroleum Industry ◽

Pressure Differential ◽

Supervisory System ◽

Pipeline Inspection ◽

Simulation Based ◽

Artificial Neural ◽

Non Destructive ◽

Nonlinear Autoregressive

Industrial pipelines must be inspected to detect typical failures, such as obstructions and deformations, during their lifetime. In the petroleum industry, the most used non-destructive technique to inspect buried pipelines is pigging. This technique consists of launching a Pipeline Inspection Gauge (PIG) inside the pipeline, which is driven by the pressure differential produced by fluid flow. The purpose of this work is to study the application of artificial neural networks to calculate the PIG’s velocity based on the pressure differential. We launch a prototype PIG inside a testing pipeline, where this PIG gathers velocity data from an odometer-based system, while a supervisory system gathers pressure data from the testing pipeline. Then we train a Multilayer Perceptron (MLP) and a Nonlinear Autoregressive Network with eXogenous Inputs (NARX) network with the gathered data to predict velocity. The results suggest it is possible to use a neural network to model the PIG’s velocity from pressure differential measurements. Our method is a new approach to the typical speed measurements based only on odometer, since the odometer is prone to fail and present poor results under some circumstances. Moreover, it can be used to provide redundancy, improving reliability of data obtained during the test.

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