An Open-Source Tool for Automated Power Grid Stress Level Prediction at Balancing Authorities

Berscheid, Alan; Makarov, Yuri V.; Hou, Zhangshuan; Diao, Ruisheng; Zhang, Yu; Samaan, Nader; Yuan, Yong J.; Zhou, Huifen

doi:10.1109/tdc.2018.8440330

Cited by 2 publications

(2 citation statements)

References 7 publications

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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%

Predicting peak day and peak hour of electricity demand with ensemble machine learning

Zhou

et al. 2022

Front. Energy Res.

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Battery energy storage systems can be used for peak demand reduction in power systems, leading to significant economic benefits. Two practical challenges are 1) accurately determining the peak load days and hours and 2) quantifying and reducing uncertainties associated with the forecast in probabilistic risk measures for dispatch decision-making. In this study, we develop a supervised machine learning approach to generate 1) the probability of the next operation day containing the peak hour of the month and 2) the probability of an hour to be the peak hour of the day. Guidance is provided on preparation and augmentation of data as well as selection of machine learning models and decision-making thresholds. The proposed approach is applied to the Duke Energy Progress system and successfully captures 69 peak days out of 72 testing months with a 3% exceedance probability threshold. On 90% of the peak days, the actual peak hour is among the 2 h with the highest probabilities.

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Section: Data Augmentationmentioning

confidence: 99%

Predicting peak day and peak hour of electricity demand with ensemble machine learning

Zhou

et al. 2022

Front. Energy Res.

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“…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%

Predicting Peak Day and Peak Hour of Electricity Demand with Ensemble Machine Learning

Fu¹,

Zhou²,

Ma³

et al. 2022

Preprint

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An Open-Source Tool for Automated Power Grid Stress Level Prediction at Balancing Authorities

Cited by 2 publications

References 7 publications

Predicting peak day and peak hour of electricity demand with ensemble machine learning

Predicting peak day and peak hour of electricity demand with ensemble machine learning

Predicting Peak Day and Peak Hour of Electricity Demand with Ensemble Machine Learning

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