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Sanlei Dang, Long Peng, Jingming Zhao, Jiajie Li and Zhengmin Kong
In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load. Firstly, a bespoke 2D data preprocessi...
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Stanley Förster, Michael Schultz and Hartmut Fricke
The air traffic is mainly divided into en-route flight segments, arrival and departure segments inside the terminal maneuvering area, and ground operations at the airport. To support utilizing available capacity more efficiently, in our contribution we f...
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Jennie Molinder, Sebastian Scher, Erik Nilsson, Heiner Körnich, Hans Bergström and Anna Sjöblom
A probabilistic machine learning method is applied to icing related production loss forecasts for wind energy in cold climates. The employed method, called quantile regression forests, is based on the random forest regression algorithm. Based on the perf...
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Fhulufhelo Walter Mugware, Caston Sigauke and Thakhani Ravele
The main source of electricity worldwide stems from fossil fuels, contributing to air pollution, global warming, and associated adverse effects. This study explores wind energy as a potential alternative. Nevertheless, the variable nature of wind introdu...
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Lei Zhang, Lun Xie, Qinkai Han, Zhiliang Wang and Chen Huang
Based on quantile regression (QR) and kernel density estimation (KDE), a framework for probability density forecasting of short-term wind speed is proposed in this study. The empirical mode decomposition (EMD) technique is implemented to reduce the noise...
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