Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control
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Title
Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control
Authors
Keywords
Confidence interval forecast, Intra-hour horizon, Solar irradiation, Smart control, Photovoltaic generation output power
Journal
ENERGY
Volume 239, Issue -, Pages 122116
Publisher
Elsevier BV
Online
2021-09-25
DOI
10.1016/j.energy.2021.122116
References
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- Assessing energy forecasting inaccuracy by simultaneously considering temporal and absolute errors
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- Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach
- (2017) Can Wan et al. IEEE TRANSACTIONS ON POWER SYSTEMS
- An ensemble prediction intervals approach for short-term PV power forecasting
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- Renewable energy sources as a new participant in ancillary service markets
- (2017) Anuj Banshwar et al. Energy Strategy Reviews
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- On the Use of Maximum Likelihood and Input Data Similarity to Obtain Prediction Intervals for Forecasts of Photovoltaic Power Generation
- (2015) Joao Gari da Silva Fonseca Junior et al. Journal of Electrical Engineering & Technology
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