Review on Classification Algorithm and Evaluation System of Machine Learning

Wu, Zhaodong; Zhang, Jun; Hu, Shengliang

doi:10.1109/icicta51737.2020.00052

Cited by 3 publications

(2 citation statements)

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“…The authors reviewed the work in [44,45] to develop measures and analysis methods for evaluating the classification models with a range of hyperparameters.…”

Section: Classification Evaluationmentioning

confidence: 99%

“…There are many hyperparameters for each classification model (see the Nomenclature section and [46][47][48] for more details). The authors investigated variation in many and reviewed research [44,45] to determine the most impactful hyperparameters, which are listed in the third column of Table 2. Values for these hyperparameters were selected, and adjustments were made to determine the results.…”

Section: Classification Modelmentioning

confidence: 99%

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A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities

O’Donnell,

2023

Energies

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New technologies, such as electric vehicles, rooftop solar, and behind-the-meter storage, will lead to increased variation in electrical load, and the location and time of the penetration of these technologies are uncertain. Power quality, reliability, and protection issues can be the result if electric utilities do not consider the probability of load scenarios that have not yet occurred. The authors’ approach to addressing these concerns started with collecting the electrical load data for an expansive and diverse set of distribution transformers. This provided approximately two-and-a-half years of data that were used to develop new methods that will enable engineers to address emerging issues. The efficacy of the methods was then assessed with a real-world test dataset that was not used in the development of the new methods. This resulted in an approach to efficiently generate stochastic electrical load forecasts for elements of distribution circuits. Methods are also described that use those forecasts for engineering analysis that predict the likelihood of distribution transformer failures and power quality events. 100% of the transformers identified as most likely to fail either did fail or identified a data correction opportunity. The accuracy of the power quality results was 92% while allowing for a balance between measures of efficiency and customer satisfaction.

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“…The authors reviewed the work in [44,45] to develop measures and analysis methods for evaluating the classification models with a range of hyperparameters.…”

Section: Classification Evaluationmentioning

confidence: 99%

Section: Classification Modelmentioning

confidence: 99%