CEO Amelia Linzey on Beca's 15-year AI journey, what we can learn from Singapore, and NZ's infrastructure deficit Singapore, at the New Zealand-Singapore 2026 Forum. Interest.co.nz is unpacking how AI will change your everyday life - the risks, the opportunities, and what to actually expect. Our new series brings you the policymakers, experts and industry
Comparative analysis of CNN–LSTM and CNN–BiLSTM hybrid deep learning models for solar radiation prediction – Scientific Reports
- Article
- Open access
- Published:
- Yogita Yashveer Raghav1,
- Parveen Kumari2,
- Prashanth kumar katta3,
- Sakshi Kathuria4,
- Manisha Mudgal5,
- M. Prabhakar6 &
- …
- Mohamed Yusuf7
Scientific Reports (2026) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
Subjects
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. Under identical preprocessing, windowing, chronological splitting, and training conditions, CNN–LSTM achieved MAE = 0.0880, RMSE = 0.1100, R2 = 0.3100, EVS = 0.3154, WI = 0.6317, and APB = 1.89%, whereas CNN–BiLSTM obtained MAE = 0.1015, RMSE = 0.1224, R2 = 0.1456, EVS = 0.1998, WI = 0.5261, and APB = 5.98%. The Skill Scores shown and the negative values for direct clearness-index prediction do not imply that the persistence reference was unattainable, but rather reveal that the results are a controlled model-to-model comparison and not evidence of state-of-the-art superiority. Reconstructed all-sky irradiance produced stronger agreement with observations (MAE = 0.4353, RMSE = 0.5417, R2 = 0.7884, EVS = 0.7965, WI = 0.9299, and APB = 3.90%). The main task of CNN–LSTM is to provide a practical balance between accuracy and efficiency in this experimental context, and further testing with other locations, more powerful baselines and probabilistic forecasting techniques is needed.
Similar content being viewed by others
Acknowledgements
The authors would like to thank their respective institutions for their extended support throughout this research work.
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissions
About this article
Cite this article
Raghav, Y.Y., Kumari, P., katta, P.k. et al. Comparative analysis of CNN–LSTM and CNN–BiLSTM hybrid deep learning models for solar radiation prediction. Sci Rep (2026). https://doi.org/10.1038/s41598-026-59621-5
Download citation
-
Received:
-
Accepted:
-
Published:
-
DOI: https://doi.org/10.1038/s41598-026-59621-5
Keywords
Related Posts
