2020
DOI: 10.1016/j.enconman.2020.112766
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A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework

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Cited by 467 publications
(142 citation statements)
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“…With the special memory structure and gated designing, LSTM has a better ability to learn long-term dependency. The structure of LSTM can be described by the following equations [36,37]:…”
Section: Long Short-term Memory Neural Networkmentioning
confidence: 99%
See 3 more Smart Citations
“…With the special memory structure and gated designing, LSTM has a better ability to learn long-term dependency. The structure of LSTM can be described by the following equations [36,37]:…”
Section: Long Short-term Memory Neural Networkmentioning
confidence: 99%
“…In health state model establishment stage, the correlation (R) is utilized as the criterion of model fitting [37].…”
Section: Establish Health State Modelmentioning
confidence: 99%
See 2 more Smart Citations
“…16 Ma et al 17 combined the Grid concept and LSTM (G-LSTM) for the forecasting of fuel cell degradation. More than that, the latest researches applied LSTM to more hot areas of prediction, for example, electricity price forecasting, 18 flood forecasting, 19 wind speed forecasting, 20 air pollution forecasting, 21 voltages forecasting, 22 demand forecasting, 23 photovoltaic power forecasting, 24 and so forth. 25,26 The LSTM was wielded to alleviate the vanished gradient in a multi-layer network architecture.…”
Section: Overview Of Studies On Carbon Price Forecasting and Other mentioning
confidence: 99%