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Comparative analysis of CNN–LSTM and CNN–BiLSTM hybrid deep learning models for solar radiation prediction

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By Parveen Kumari, Sakshi Kathuria, M. N. Prabhakar, Mohamed Yusuf, Yogita Yashveer Raghav, Prashanth kumar katta, Manisha Mudgal
| Nature Verified
Abstract Solar radiation forecasting is a complex task since the radiation signal is nonlinear, intermittent and is significantly influenced by meteorological variability, which makes it vital for PV planning, renewable energy planning and stability of the smart grid. In this work, a replicable comparison between CNN–LSTM and CNN–BiLSTM models for one-step ahead solar clearness-index forecasting based on multivariate climate variables from NASA POWER dataset for Delhi, India, is presented.
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