2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D) 2018
DOI: 10.1109/tdc.2018.8440330
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An Open-Source Tool for Automated Power Grid Stress Level Prediction at Balancing Authorities

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Cited by 2 publications
(2 citation statements)
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“…Therefore, data augmentation is needed to generate sufficient data for model training and reliable testing (Chawla et al, 2002). An ANN model for day-ahead load forecast has been developed using load and weather attributes (Berscheid et al, 2018). The model is developed using Matlab's Deep Learning Toolbox.…”
Section: Data Augmentationmentioning
confidence: 99%
“…Therefore, data augmentation is needed to generate sufficient data for model training and reliable testing (Chawla et al, 2002). An ANN model for day-ahead load forecast has been developed using load and weather attributes (Berscheid et al, 2018). The model is developed using Matlab's Deep Learning Toolbox.…”
Section: Data Augmentationmentioning
confidence: 99%
“…Therefore, data augmentation is needed to generate sufficient data for model training and reliable testing. An ANN model for day-ahead load forecast has been developed using load and weather attributes(Berscheid, Makarov, Hou, Diao, Zhang, Samaan, Yuan & Zhou, 2018).The developed model has been cross-validated with historical load and weather data sets for major U.S. Balancing Authorities.…”
mentioning
confidence: 99%