2019
DOI: 10.1016/j.energy.2019.116225
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Photovoltaic power forecasting based LSTM-Convolutional Network

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Cited by 316 publications
(129 citation statements)
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“…The problems of gradient vanishing and the explosion of long-term dependencies have been improved by replacing the LSTM with basic hidden neurons in the RNN structure. As shown in Figure 2, the LSTM includes the forget gate, input gate, update gate, and output gate in the principle structure [52]. The LSTM network implements temporary memory through switch gates to prevent gradient vanishing.…”
Section: Long Short Term Memory (Lstm)mentioning
confidence: 99%
See 1 more Smart Citation
“…The problems of gradient vanishing and the explosion of long-term dependencies have been improved by replacing the LSTM with basic hidden neurons in the RNN structure. As shown in Figure 2, the LSTM includes the forget gate, input gate, update gate, and output gate in the principle structure [52]. The LSTM network implements temporary memory through switch gates to prevent gradient vanishing.…”
Section: Long Short Term Memory (Lstm)mentioning
confidence: 99%
“…The LSTM network implements temporary memory through switch gates to prevent gradient vanishing. The main computation formula of the LSTM is as follows [52,53]:…”
Section: Long Short Term Memory (Lstm)mentioning
confidence: 99%
“…Recurrent neural networks (RNNs) [13], particularly those with a long-short term memory unit (LSTM) [40], [41] are the preferred single time-series forecasters. The effectiveness of these networks can be evaluated in terms of the recurrent connections, which permit the network to access all historical time series values.…”
Section: Convolutional Neural Networkmentioning
confidence: 99%
“…1. LSTM: LSTM has achieved great performance in work about power quality [43], [44]. This is a typical recurrent neural network (RNN) with a memory unit consisting of a gated input, a gated output, and a gated feedback loop [45].…”
Section: ) Comparison With State-of-the-art Dnnsmentioning
confidence: 99%