Explainable Lightweight Deep Learning for Solar Radiation Prediction Using Mobilenet-Bilstm with Multi-Head Attention
Abstract
As the global demand for sustainable energy grows, the importance of solar energy as a clean and inexhaustible source of energy has become increasingly prominent. However, the efficiency and yield of solar power generation are directly affected by the solar radiation intensity, so accurate prediction of solar radiation is crucial to improve the performance of solar power generation systems. This paper proposes a hybrid Mobilenet-BiLSTM deep learning model combined with multi-head attention mechanism for solar radiation prediction. The lightweight model MobileNet is used to extract image features, and the BiLSTM model is combined to process time series features. Moreover, a multi-head attention mechanism is introduced to enhance the model's ability to capture the correlation of input sequences at different positions, so as to improve the prediction accuracy. In the model performance evaluation, the hybrid model performs well on multiple key indicators, with Mean Absolute Error of 0.2355, Mean Square Error of 0.1159, and Root Mean Square Error of 0.3405. In addition, Explainable Artificial Intelligence techniques, including Grad-CAM, SHAP, and LIME, are integrated to interpret the predictions of the model and improve the transparency and reliability of the model. The experimental results show that the hybrid model has achieved remarkable results in prediction accuracy, model interpretability and practical application, which provides a new solution for the efficient utilization of solar energy resources.
Keywords
Solar Radiation PredictionDeep LearningMobileNetBiLSTMAttention MechanismMulti-modal 1
References
M. Dada et al., ‘Functional materials for solar thermophotovoltaic devices in energy conversion applications: a review’, Front. Energy Res., vol. 11, Jun. 2023, doi: 10.3389/fenrg.2023.1124288. Z. Er, Z. Rouabah, and I. B. Turna, ‘A Modal Study on the Estimation of Daily Global Solar Radiation from Sunshine Duration’, ACTA Phys. Pol. A, vol. 132, no. 3, pp. 1070–1075, Sep. 2017, doi: 10.12693/APhysPolA.132.1070. J. Calbó and J. Sabburg, ‘Feature Extraction from Whole-Sky Ground-Based Images for Cloud- Type Recognition’, J. Atmospheric Ocean. Technol., vol. 25, no. 1, pp. 3–14, Jan. 2008, doi: 10.1175/2007JTECHA959.1. I. Sansa, Z. Boussaada, and N. M. Bellaaj, ‘Solar Radiation Prediction Using a Novel Hybrid Model of ARMA and NARX’, ENERGIES, vol. 14, no. 21, p. 6920, Nov. 2021, doi: 10.3390/en14216920. D. Kaskaoutis and J. Polo, ‘Editorial for the Special Issue “Solar Radiation, Modeling, and Remote Sensing”’, Remote Sens., vol. 11, no. 10, Art. no. 10, Jan. 2019, doi: 10.3390/rs11101198. R. J. Zamora, E. G. Dutton, M. Trainer, S. A. McKeen, J. M. Wilczak, and Y.-T. Hou, ‘The Accuracy of Solar Irradiance Calculations Used in Mesoscale Numerical Weather Prediction’, Mon. Weather Rev., vol. 133, no. 4, pp. 783–792, Apr. 2005, doi: 10.1175/MWR2886.1. S.-Y. Wang, J. Qiu, and F.-F. Li, ‘Hybrid Decomposition-Reconfiguration Models for Long- Term Solar Radiation Prediction Only Using Historical Radiation Records’, Energies, vol. 11, no. 6, Art. no. 6, Jun. 2018, doi: 10.3390/en11061376. T. Khatib and W. Elmenreich, ‘A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network’, Int. J. PHOTOENERGY, vol. 2015, p. 968024, 2015, doi: 10.1155/2015/968024. L. Huang, J. Kang, M. Wan, L. Fang, C. Zhang, and Z. Zeng, ‘Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events’, Front. Earth Sci., vol. 9, Apr. 2021, doi: 10.3389/feart.2021.596860. Z. He, X. Zhang, M. Li, S. Wang, and G. Xiao, ‘A novel solar radiation forecasting model based on time series imaging and bidirectional long short-term memory network’, Energy Sci. Eng., vol. 12, no. 11, pp. 4876–4893, Nov. 2024, doi: 10.1002/ese3.1875. H. Zhao, Q. Wu, S. Hu, H. Xu, and C. N. Rasmussen, ‘Review of energy storage system for wind power integration support’, Appl. Energy, vol. 137, pp. 545–553, Jan. 2015, doi: 10.1016/j.apenergy.2014.04.103. I. Purohit and P. Purohit, ‘Performance assessment of grid-interactive solar photovoltaic projects under India’s national solar mission’, Appl. Energy, vol. 222, pp. 25–41, Jul. 2018, doi: 10.1016/j.apenergy.2018.03.135. M. Aslam, K. H. Seung, S. Jae Lee, J.-M. Lee, S. Hong, and E. H. Lee, ‘Long-term Solar Radiation Forecasting using a Deep Learning Approach-GRUs’, in 2019 IEEE 8th International Conference on Advanced Power System Automation and Protection , Oct. 2019, pp. 917–920. doi: 10.1109/APAP47170.2019.9224661. V. Gider, C. Budak, D. Izci, and S. Ekinci, ‘Daily Solar Radiation Prediction Using LSTM Neural Networks’, in 2022 Global Energy Conference , Oct. 2022, pp. 168–172. doi: 10.1109/GEC55014.2022.9987055. U. T. Kartini, Hariyati, W. Aribowo, and A. L. Wardani, ‘Development Hybrid Model Deep Learning Neural Network (DL-NN) For Probabilistic Forecasting Solar Irradiance on Solar Cells To Improve Economics Value Added’, in 2022 Fifth International Conference on Vocational Education and Electrical Engineering , Sep. 2022, pp. 151–156. doi: 10.1109/ICVEE57061.2022.9930352. S. Ghimire, R. C. Deo, D. Casillas-Pérez, S. Salcedo-Sanz, E. Sharma, and M. Ali, ‘Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction’, Measurement, vol. 202, p. 111759, Oct. 2022, doi: 10.1016/j.measurement.2022.111759. 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. Happy. N. M, 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. Nkanta. H. M, 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 C. N. Obiora, A. Ali, and A. N. Hasan, ‘Efficient Method of Finding the Best Time Interval for Predicting Short Term Solar Radiation Using CNN-LSTM Model’, in 2022 11th International Conference on Power Science and Engineering , Sep. 2022, pp. 159–163. doi: 10.1109/ICPSE56329.2022.9935441. Grace. U. N, 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. 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 N. U. Ugochi, 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 H. Loutfi, A. Bernatchou, Y. Raoui, and R. Tadili, ‘Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks’, Int. J. Photoenergy, vol. 2017, no. 1, p. 4025283, 2017, doi: 10.1155/2017/4025283. H. Bak?r, ‘Prediction of daily global solar radiation in different climatic conditions using metaheuristic search algorithms: a case study from Türkiye’, Environ. Sci. Pollut. Res., vol. 31, no. 30, pp. 43211–43237, Jun. 2024, doi: 10.1007/s11356-024-33785-x. A. Kasaeian, M. Mehrpooya, M. Aghaie, and M. H. Ahmadi, ‘Solar radiation prediction based on ICA and HGAPSO for Kuhin City, Iran’, Mech. Ind., vol. 17, no. 5, Art. no. 5, 2016, doi: 10.1051/meca/2015100. L. Huang, J. Kang, M. Wan, L. Fang, C. Zhang, and Z. Zeng, ‘Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Clima
More Articles from INTERNATIONAL JOURNAL OF ENGINEERING AND MODERN TECHNOLOGY
Mobile Network Coverage and Road Surface Condition as Joint Constraints on Digitally Booked Transport Beyond State Capitals
Author: Oladimeji Basit Alaka,, Oluwabusola Ajayi, Olayemi Bolaji
Cross-Functional Legal Education as Risk Mitigation: Empirical Analysis of Contract Error Reduction in Engineering Teams
Author: Cyril Chimelie Anichukwueze, Michael Ominyi, Ngozi Samuel Uzougbo, Blessing Chika, Jones
Multimodal Transit Centre in Peri -Urban Areas: Lessons for Lagos Ogun Corridor Ijoko
Author: Aseminaso.T. Precious, Adibe. Nkeiruka, Chinwennwo. P. Ohochuku
Multimodal Transit Centre in Peri -Urban Areas: Lessons for Lagos Ogun Corridor Ijoko
Author: Aseminaso.T. Precious, Adibe. Nkeiruka, Chinwennwo. P. Ohochuku
Mechanical Performance and Optimum Proportioning of Waste- PET and Crumb-Rubber Bituminous Concrete for Heavy Traffic
Author: Ndem U., Arinze E.E., Awodeyi A.