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Full-Text Articles in Engineering
Solar Irradiance Forecasting Using Deep Neural Networks, Ahmad Alzahrani, Pourya Shamsi, Cihan H. Dagli, Mehdi Ferdowsi
Solar Irradiance Forecasting Using Deep Neural Networks, Ahmad Alzahrani, Pourya Shamsi, Cihan H. Dagli, Mehdi Ferdowsi
Electrical and Computer Engineering Faculty Research & Creative Works
Predicting solar irradiance has been an important topic in renewable energy generation. Prediction improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. The irradiance can be predicted using statistical methods such as artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average (ARMA). However, they either lack accuracy because they cannot capture long-term dependency or cannot be used with big data because of the scalability. This paper presents a method to predict the solar irradiance using deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model without specifying …
An Open Framework For Highly Concurrent Real-Time Hardware-In-The-Loop Simulation, Ryan C. Underwood, Bruce M. Mcmillin, Mariesa Crow
An Open Framework For Highly Concurrent Real-Time Hardware-In-The-Loop Simulation, Ryan C. Underwood, Bruce M. Mcmillin, Mariesa Crow
Computer Science Faculty Research & Creative Works
Hardware-in-the-loop (HIL) real-time simulation is becoming a significant tool in prototyping complex, highly available systems. The HIL approach permits testing of hardware prototypes of components that would be extremely costly or difficult to test in the deployed environment. In power system simulation, key issues are the ability to wrap the systems of equations (such as Partial Differential Equations) describing the deployed environment into real-time software models, provide low synchronization overhead between the hardware and software, and reduce reliance on proprietary platforms. This paper introduces an open source HIL simulation framework that can be ported to any standard Unix-like system on …
Using Neural Networks To Estimate Wind Turbine Power Generation, Shuhui Li, Donald C. Wunsch, Edgar O'Hair, Michael G. Giesselmann
Using Neural Networks To Estimate Wind Turbine Power Generation, Shuhui Li, Donald C. Wunsch, Edgar O'Hair, Michael G. Giesselmann
Electrical and Computer Engineering Faculty Research & Creative Works
This paper uses data collected at Central and South West Services Fort Davis wind farm to develop a neural network based prediction of power produced by each turbine. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes—lower-than-expected wind power may be an early indicator of a need for maintenance. In this paper, characteristics of wind power generation are first evaluated in order to establish the relative importance for the neural network. A …
Comparison Of Fdtd Algorithms For Subcellular Modeling Of Slots In Shielding Enclosures, Kuang-Ping Ma, Min Li, James L. Drewniak, Todd H. Hubing, Thomas Van Doren
Comparison Of Fdtd Algorithms For Subcellular Modeling Of Slots In Shielding Enclosures, Kuang-Ping Ma, Min Li, James L. Drewniak, Todd H. Hubing, Thomas Van Doren
Electrical and Computer Engineering Faculty Research & Creative Works
Subcellular modeling of thin slots in the finite-difference time-domain (FDTD) method is investigated. Two subcellular algorithms for modeling thin slots with the FDTD method are compared for application to shielding end osures in electromagnetic compatibility (EMC). The stability of the algorithms is investigated, and comparisons between the two methods for slots in planes, and slots in loaded cavities are made. Results for scattering from a finite-length slot in an infinite plane employing one of the algorithms are shown to agree well with published experimental results, and power delivered to an enclosure with a slot agree well with results measured for …