Short-Term Load Forecasting Using Machine Learning Algorithms
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-1699-3_35
Reference19 articles.
1. Li MS, Wu JL, Ji TY, Wu QH, Zhu L (2015) Short-term load forecasting using support vector regression-based local predictor. In: IEEE power and energy society general meeting. https://doi.org/10.1109/PESGM.2015.7285911
2. Hobbs BF (1999) Analysis of the value for unit commitment of improved load forecasts. IEEE Trans Power Syst 14(4):1342–1348. https://doi.org/10.1109/59.801894
3. Zhang C, Chen Z, Zhou J (2020) Research on short-term load forecasting using K-means clustering and catboost integrating time series features. In: Research on short-term load forecasting using K-means clustering and catboost integrating time series features
4. Bhanu CVK, Sudheer G, Radhakrishna C, Phanikanth V (2008) Day-ahead electricity price forecasting using wavelets and weighted nearest neighbourhood. In: 2008 joint international conference on power system technology POWERCON and IEEE power India conference, POWERCON 2008. https://doi.org/10.1109/ICPST.2008.4745359
5. Zhang W (2022) Short-term load forecasting of power model based on CS-Catboost algorithm. In: IEEE joint international information technology and artificial intelligence conference (ITAIC), pp 2295–2299. https://doi.org/10.1109/ITAIC54216.2022.9836483
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