Machine learning-based very short-term load forecasting in microgrid environment: evaluating the impact of high penetration of PV systems
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Electrical and Electronic Engineering
Link
https://link.springer.com/content/pdf/10.1007/s00202-022-01509-4.pdf
Reference33 articles.
1. Luo J, Hong T, Yue M (2018) Real-time anomaly detection for very short-term load forecasting. J Mod Power Syst Clean Energy 6:235–243. https://doi.org/10.1007/s40565-017-0351-7
2. Koprinska I, Rana M, Agelidis VG (2015) Correlation and instance based feature selection for electricity load forecasting. Knowl Based Syst 82:29–40. https://doi.org/10.1016/j.knosys.2015.02.017
3. Rafati A, Joorabian M, Mashhour E, Shaker HR (2021) High dimensional very short-term solar power forecasting based on a data-driven heuristic method. Energy 219:119647. https://doi.org/10.1016/j.energy.2020.119647
4. Vrettos E, Kara EC, Stewart EM, Roberts C (2011) Estimating PV power from aggregate power measurements within the distribution grid. J Renew Sustain Energy. https://doi.org/10.1063/1.5094161
5. Kaur A, Nonnenmacher L, Pedro HTCC, Coimbra CFMM (2016) Benefits of solar forecasting for energy imbalance markets. Renew Energy 86:819–830. https://doi.org/10.1016/j.renene.2015.09.011
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