Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM

More and more photovoltaic (PV) power generation is incorporated into the grid. However, the intermittence and fluctuation of solar energy have brought huge challenges to the safe and stable operation of the power grid. PV power forecasting is one of the effective ways to solve the above problems, s...

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Veröffentlicht in:	Energy (Oxford) Jg. 246; S. 123403
Hauptverfasser:	Huang, Xiaoqiao, Li, Qiong, Tai, Yonghang, Chen, Zaiqing, Liu, Jun, Shi, Junsheng, Liu, Wuming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 01.05.2022 Elsevier BV
Schlagworte:	Accuracy Algorithms Artificial neural networks Back propagation networks Bi-LSTM Conditional generative adversarial network Convolutional neural networks data collection Deep learning Electric power grids energy Feature extraction Forecasting Generative adversarial networks Long short-term memory Machine learning Neural networks Optimization Performance evaluation Photovoltaic cells Photovoltaics power generation prediction Prediction models PV power Forecasting Random noise Recurrent neural networks Regression models Solar energy Support vector machines Time series time series analysis Bi-LSTM PV power Forecasting Conditional generative adversarial network Convolutional neural networks
ISSN:	0360-5442, 1873-6785
Online-Zugang:	Volltext
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