Utilizing Nonlinear Autoregressive with Exogenous Input Neural Networks to Evaluate the Thermal Flywheel Effect Along Intake Shafts at Nevada Mines

2021 ◽  
Author(s):  
Kyle A. Scalise ◽  
Karoly (Charles) Kocsis
Keyword(s):  
Neural Networks ◽  
Nonlinear Autoregressive

Congestion Prediction System With Artificial Neural Networks

2020 ◽  
Vol 12 (3) ◽  
pp. 28-43
Author(s):  
Fatma Gumus ◽  
Derya Yiltas-Kaplan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Prediction Algorithm ◽  
Learning Stage ◽  
Network Algorithms ◽  
Prediction Scheme ◽  
Machine Learning Approach ◽  
Congestion Prediction ◽  
Nonlinear Autoregressive

Software Defined Network (SDN) is a programmable network architecture that provides innovative solutions to the problems of the traditional networks. Congestion control is still an uncharted territory for this technology. In this work, a congestion prediction scheme has been developed by using neural networks. Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm was performed on the data collected from the OMNET++ simulation. The novelty of this study also covers the implementation of mRMR in an SDN congestion prediction problem. After evaluating the relevance scores, two highest ranking features were used. On the learning stage Nonlinear Autoregressive Exogenous Neural Network (NARX), Nonlinear Autoregressive Neural Network, and Nonlinear Feedforward Neural Network algorithms were executed. These algorithms had not been used before in SDNs according to the best of the authors knowledge. The experiments represented that NARX was the best prediction algorithm. This machine learning approach can be easily integrated to different topologies and application areas.

scholarly journals Nonlinear Autoregressive Neural Networks to Predict Hydraulic Fracturing Fluid Leakage into Shallow Groundwater

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 841 ◽  
Author(s):  
Reza Taherdangkoo ◽  
Alexandru Tatomir ◽  
Mohammad Taherdangkoo ◽  
Pengxiang Qiu ◽  
Martin Sauter
Keyword(s):  
Neural Networks ◽  
Fluid Flow ◽  
Hydraulic Fracturing ◽  
Flow Rate ◽  
Shallow Groundwater ◽  
Fluid Flow Rate ◽  
Shallow Aquifers ◽  
Fracturing Fluid ◽  
Upward Migration ◽  
Nonlinear Autoregressive

Hydraulic fracturing of horizontal wells is an essential technology for the exploitation of unconventional resources, but led to environmental concerns. Fracturing fluid upward migration from deep gas reservoirs along abandoned wells may pose contamination threats to shallow groundwater. This study describes the novel application of a nonlinear autoregressive (NAR) neural network to estimate fracturing fluid flow rate to shallow aquifers in the presence of an abandoned well. The NAR network is trained using the Levenberg–Marquardt (LM) and Bayesian Regularization (BR) algorithms and the results were compared to identify the optimal network architecture. For NAR-LM model, the coefficient of determination (R2) between measured and predicted values is 0.923 and the mean squared error (MSE) is 4.2 × 10−4, and the values of R2 = 0.944 and MSE = 2.4 × 10−4 were obtained for the NAR-BR model. The results indicate the robustness and compatibility of NAR-LM and NAR-BR models in predicting fracturing fluid flow rate to shallow aquifers. This study shows that NAR neural networks can be useful and hold considerable potential for assessing the groundwater impacts of unconventional gas development.

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 Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhongxian Men ◽  
Eugene Yee ◽  
Fue-Sang Lien ◽  
Zhiling Yang ◽  
Yongqian Liu
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Northern China ◽  
Short Term ◽  
Artificial Neural ◽  
Nonlinear Autoregressive ◽  
Power Forecasting

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

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