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- W4302350288 abstract "The accuracy of electric load forecasting is crucial when working on applications in power grid decision-making and operation. Due to the non-linear and stochastic behaviour of customers, the electric load profile is a complicated signal. In this paper, authors propose machine learning based automated system for electricity load forecasting, taking into consideration various variable factors that have an impact on the future electricity load demand. Three machine learning algorithms are used for evaluation of the proposed framework. The algorithms are evaluated on electricity load data collected from eastern region of Ontario, integrated with the weather and population data of the region. The Light GBM algorithm comparatively performs best with mean absolute error of 0.156. The developed system can be used for more accurate and efficient load forecasting applications." @default.
- W4302350288 created "2022-10-06" @default.
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- W4302350288 date "2022-06-01" @default.
- W4302350288 modified "2023-10-18" @default.
- W4302350288 title "Comprehensive Electric load forecasting using ensemble machine learning methods" @default.
- W4302350288 cites W1930624869 @default.
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- W4302350288 doi "https://doi.org/10.1109/iwssip55020.2022.9854390" @default.
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