Learning Cloud Shadow Dynamics from Sky Imagery for Short- Term Photovoltaic Power Forecasting
Abstract
This research tackles the critical challenge of predicting photovoltaic power output fluctuations caused by cloud shadows, a major obstacle for grid stability. We propose a novel hybrid CNN- LSTM deep learning framework to analyze sky imagery, track cloud movement, and forecast irradiance impacts. The methodology processes satellite images, using Convolutional Neural Networks to extract spatial features of cloud formations and Long Short-Term Memory networks to model their temporal dynamics. The model was trained on the "Solar Energy Forecasting using Low-Res Sky Images" dataset from Wollongong, Australia, which pairs sky imagery with PV power measurements. Our hybrid model achieved superior performance with a Mean Squared Error of 0.0032, Mean Absolute Error of 0.042, and Mean Absolute Percentage Error of 17.48%. This represents a substantial improvement over standalone CNN (MAE: 0.6109) and LSTM (MAE: 1.0986) models. Although overfitting was addressed via regularization, the architecture successfully captured both spatial and temporal cloud patterns. This work demonstrates the effective application of deep learning for solar forecasting, offering a solution to enhance the reliability of solar power systems and support broader renewable energy integration.
Keywords
Deep LearningSolar Energy ForecastingCloud Shadow MappingCNN-LSTM Hybrid ModelSky Image AnalysisRenewable EnergyPhotovoltaic Power Prediction
References
S. Tajjour et al., 'Short-Term Solar Irradiance Forecasting Using Deep Learning Techniques: A Comprehensive Case Study,' IEEE Access, vol. 11, pp. 119851-119861, 2023, doi: 10.1109/ACCESS.2023.3325292. X. Su, T. Li, C. An, and G. Wang, 'Prediction of Short-Time Cloud Motion Using a Deep- Learning Model,' Atmosphere, vol. 11, no. 11, p. 1151, 2020, doi: 10.3390/atmos11111151. F. Marchesoni-Acland et al., 'Deep learning methods for intra-day cloudiness prediction using geostationary satellite images in a solar forecasting framework,' Solar Energy, vol. 262, p. 111820, 2023, doi: 10.1016/j.solener.2023.111820. A. Ryu et al., 'Preliminary analysis of short-term solar irradiance forecasting by using total-sky imager and convolutional neural network,' in IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), 2019, doi: 10.1109/GTDAsia.2019.8715984. Y. Pi et al., 'Short-term Solar Irradiation Prediction Model Based on WCNN_ALSTM,' in IEEE International Conference on Dependable, Autonomic and Secure Computing, 2021, pp. 405-412, doi: 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00075. L. Cheng et al., 'Short-term Solar Power Prediction Learning Directly from Satellite Images With Regions of Interest,' IEEE Transactions on Sustainable Energy, vol. 13, no. 1, pp. 629-639, 2022, doi: 10.1109/TSTE.2021.3123476. Y. Xu et al., 'Cloud Displacement Vector Calculation in Satellite Images Based on Cloud Pixel Spatial Aggregation and Edge Matching for PV Power Forecasting,' in IEEE Sustainable Power and Energy Conference , 2020, pp. 112-119, doi: 10.1109/iSPEC50848.2020.9351115. P. M. P. Garniwa et al., 'Intraday forecast of global horizontal irradiance using optical flow method and long short-term memory model,' Solar Energy, vol. 252, pp. 234-251, 2023, doi: 10.1016/j.solener.2023.01.037. Z. Si et al., 'Hybrid Solar Forecasting Method Using Satellite Visible Images and Modified Convolutional Neural Networks,' IEEE Transactions on Industry Applications, vol. 57, no. 1, pp. 5-16, 2021, doi: 10.1109/TIA.2020.3028558. L. Dissawa et al., 'Sky Images and PV Power Measurements for Irradiance Forecasting,' Mendeley Data, V2, 2021, doi: 10.17632/cb8t8np9z3.2. S. Hochreiter and J. Schmidhuber, 'Long Short-Term Memory,' Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997, doi: 10.1162/neco.1997.9.8.1735. D. Bahdanau, K. Cho, and Y. Bengio, 'Neural Machine Translation by Jointly Learning to Align and Translate,' in International Conference on Learning Representations , 2015. arXiv:1409.0473. A. G. Howard et al., 'MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,' 2017. arXiv:1704.04861. M. Xia, W. Wang, and Z. Pan, 'SkyCast: Solar Irradiance Forecasting System Based on Deep Learning and Cloud Computing,' IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1487-1498, 2022, doi: 10.1109/TSG.2021.3136566. Y. Zhang, J. Zhang, Y. Korukonda, and M. Kankanhalli, 'Secure and Efficient Sky Image- Based Solar Irradiance Prediction with Deep Learning,' in IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, 2019, pp. 1-6, doi: 10.1109/SmartGridComm.2019.8909708. J. Mangharam and A. Rana, 'Artificial neural networks for solar radiation prediction,' in Conference on Computational Intelligence and Communication Networks, 2015, pp. 396- 401, doi: 10.1109/CICN.2015.83. A. Alzahrani, S. Shamshirband, A. Pourhoseingholi, A. Alharbi, and K. Khedif, 'Solar radiation prediction using deep LSTM networks,' IEEE Access, vol. 8, pp. 7189-7199, 2020, doi: 10.1109/ACCESS.2020.2964747. F. Wang, Z. Xuan, Z. Zhen, K. Li, T. Wang, and M. Shi, 'A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework,' Energy Conversion and Management, vol. 196, pp. 1310-1321, 2019, doi: 10.1016/j.enconman.2019.06.076. H. Wang, Z. Lei, X. Zhang, B. Zhou, and J. Peng, 'A review of deep learning for renewable energy forecasting,' Energy Conversion and Management, vol. 198, p. 111799, 2019, doi: 10.1016/j.enconman.2019.111799. P. Mathiesen, C. Collier, and J. Kleissl, 'A high-resolution, cloud-assimilating numerical weather prediction model for solar irradiance forecasting,' Solar Energy, vol. 92, pp. 47- 61, 2013, doi: 10.1016/j.solener.2013.02.018. M. Diagne, M. David, P. Lauret, J. Boland, and N. Schmutz, 'Review of solar irradiance forecasting methods and a proposition for small-scale insular grids,' Renewable and Sustainable Energy Reviews, vol. 27, pp. 65-76, 2013, doi: 10.1016/j.rser.2013.06.042. R. H. Inman, H. T. Pedro, and C. F. Coimbra, 'Solar forecasting methods for renewable energy integration,' Progress in Energy and Combustion Science, vol. 39, no. 6, pp. 535-576, 2013, doi: 10.1016/j.pecs.2013.06.002. Li, X., Wang, Y., Monday, H.N. and Nneji, G.U., 2025. A novel residual learning of multi-scale feature extraction model for the classification of rice grain varieties. Computers and Electronics in Agriculture, 237, p.110491. Monday, H.N., Nneji, G.U., Hossin, M.A., Mark, K.D., Umana, E.S., Mgbejime, G.T. and Li, J., 2025. Enhancing ECG Classification in Cardiac Diagnostics: A Novel Approach Using Adaptive Focal Cross-Entropy Loss Function. IEEE Journal of Biomedical and Health Informatics. Nneji, G. U., Monday, H. N., Pathapati, V. S. R., Nahar, S., Mgbejime, G. T., Umana, E. S., & Hossin, M. A. (2025). FFS-IML: fusion-based statistical feature selection for machine learning-driven interpretability of chronic kidney disease. International Journal of Machine Learning and Cybernetics, 1-34. H. N. Monday, J. Li, G. U. Nneji, C. C. Ukwuoma, J. Cai, I. Chikwendu, and A. Oluwasanmi, “A wavelet convolutional capsule network with modified super resolution generative adversarial network for fault diagnosis and classification”, Complex & Intelligent Systems, vol. 8, no. 1, pp. 1–15, Apr. 2022. doi: 10.1007/s40747-022-00733-6. H. N. Monday, J. Li, G. U. Nneji, M. A. Hossin, S. Nahar, J. Jackson, and I. A. Chikwendu, “WMR-DepthwiseNet: A Wavelet Multi-Resolution Depthwise Separable Convolutional Neural Network for COVID-19 Diagnosis”, Diagnostics, vol. 12, no. 3, p. 765, Mar. 2022, https://doi.org/10.3390/diagnostics12030765 H. N. Monday, J. Li, G. U. Nneji, M. A. Hossin, S. Nahar, J. Jackson, and C. J. Ejiyi, “COVID- 19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network”, Diagnostics, vol. 12, no. 3, p. 741, Mar. 2022, https://doi.org/10.3390/DIAGNOSTICS12030741 G. U. Nneji, J. Cai, H. N. Monday, M. A. Hossin, S. Nahar, J. Jackson, and J. Deng, “Fine-tuned Siamese Network with Modified Enhanced Super-Resolution GAN Plus Based on Low Quality Chest X-Ray Images for COVID-19 Identification”, Diagnostics, vol. 12, no. 3, Mar. 2022, https://doi.org/10.3390/diagnostics12030717. G. U. Nneji, J. Deng, H. N. Monday, J. Cai, M. A. Hossin, S. Nahar, and J. Jackson, “COVID-19 Identification from Low-Quality Computed Tomography Using a Modified Enhanced Super-Resolution Generative Adversarial Network Plus and Siamese Capsule Network”, Healthcare, vol. 10, no. 2, p. 403, Feb. 2022, https://doi.org/10.3390/healthcare10020403. G. U. Nneji, J. Cai, J. Deng, M. A. Hossin, S. Nahar, and J. Jackson, “Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans”, Diagnostics, vol. 12, no. 2, p. 540, Feb. 2022, https://doi.org/10.3390/diagnostics12020540. G. U. Nneji, J. Cai, J. Deng, H. N. Monday, E. C. James, and C. C. Ukwuoma, “Multi-Channel Based Image Processing Scheme for Pneumonia Identification”, Diagnostics, vol. 12, no. 2, p. 325, Jan. 2022, https://doi.org/10.3390/diagnostics12020325
More Articles from INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY
A Flexible Edge Search Algorithm for Graceful Tree Labeling
Author: Michael Arnold and Fabio Vitor
Advances in Algorithmic Contract Scoring for Pre-Negotiation Yield Optimization and Risk Retention
Author: Ngozi Samuel Uzougbo, Michael Ominyi, Cyril Chimelie Anichukwueze, Blessing, Chika Jones
DevTest flow: Designing a Scalable Continuous Testing Pipeline for High-Velocity Software Delivery
Author: Lawal Ahmed Oladimeji, Achori Busayo, Akeju BusayoZainab, Saka Samson, Damilare, Mbah Demian Chidi, Runsewe Similoluwa Mayowa, Oladiti Luqman, Abiodun
Assessing Blackboard LMS Implementation, Adoption Barriers and Digital Transformation in Tertiary Institutions in Kogi State, Nigeria
Author: Okolo Clement, Eluemuno
Intelligent Cybercrime Prediction and Control Model Using Hybrid Machine Learning Algorithms for Ddos and Phishing Attack Mitigation
Author: Chukumeka Gift Iroanwusi, Davies Isobo Nelson