Very Short-Term Power Forecasting for Photovoltaic Power Plants Using a Simple LSTM Model Based on Short-Term Historical Datasets: Case Study
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54674-7_3
Reference34 articles.
1. Mellit, A., Massi Pavan, A., Ogliari, E., Leva, S., Lughi, V.: Advanced methods for photovoltaic output power forecasting: a review. Appl. Sci. 10(2), 487 (2020)
2. Hossain, M., Mekhilef, S., Danesh, M., Olatomiwa, L., Shamshirband, S.: Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems. J. Clean. Prod. 167, 395–405 (2017)
3. Raza, M.Q., Nadarajah, M., Ekanayake, C.: On recent advances in PV output power forecast. Sol. Energy 136, 125–144 (2016)
4. Sobri, S., Koohi-Kamali, S., Rahim, N.A.: Solar photovoltaic generation forecasting methods: a review. Energy Convers. Manag. 156, 459–497 (2018)
5. Rana, M., Koprinska, I., Agelidis, V.G.: Univariate and multivariate methods for very short-term solar photovoltaic power forecasting. Energy Convers. Manag. 121, 380–390 (2016)
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