Improving Short-Term Solar Power Prediction through a Hybrid Deep Learning Model

Sara Fennane; Houda Kacimi; Hamza Mabchour; Fatehi ALtalqi; Aziz El Hazmir; Adil Echchelh

doi:10.48084/etasr.15965

Authors

Sara Fennane Laboratory of Electronic Systems Information Processing Mechanics and Energetics, Ibn Tofail University, Kenitra, Morocco
Houda Kacimi Laboratory of Electronic Systems Information Processing Mechanics and Energetics, Ibn Tofail University, Kenitra, Morocco
Hamza Mabchour Laboratory of Electronic Systems Information Processing Mechanics and Energetics, Ibn Tofail University, Kenitra, Morocco
Fatehi ALtalqi Laboratory of Electronic Systems Information Processing Mechanics and Energetics, Ibn Tofail University, Kenitra, Morocco
Aziz El Hazmir Laboratory of Electronic Systems Information Processing Mechanics and Energetics, Ibn Tofail University, Kenitra, Morocco
Adil Echchelh Laboratory of Electronic Systems Information Processing Mechanics and Energetics, Ibn Tofail University, Kenitra, Morocco

Volume: 16 | Issue: 2 | Pages: 32825-32832 | April 2026 | https://doi.org/10.48084/etasr.15965

Received: 2 November 2025 | Revised: 30 November 2025 and 12 December 2025 | Accepted: 15 December 2025 | Online: 2 February 2026

Corresponding author: Sara Fennane

Abstract

Reliable solar energy is fundamental to sustaining clean energy systems; however, its inherent variability poses significant challenges to the stability and operational reliability of modern power grids. Accurate short-term Photovoltaic (PV) power forecasting is therefore essential for effective energy management and the smooth operation of smart grid infrastructures. In this work, we introduce an adapted hybrid Long Short-Term Memory–Temporal Convolutional Network (LSTM-TCN) architecture that represents the core methodological contribution of the study. This hybrid design leverages the ability of LSTM networks to capture long-term temporal dependencies while exploiting the expanded receptive field and efficient parallelization offered by TCNs. This architecture is applied for the first time to PV power forecasting under Moroccan climatic conditions. The proposed model is trained and validated using a decade-long real-world dataset (2013–2023) collected in Dakhla, Morocco, a region characterized by distinctive meteorological patterns that enhance the robustness and relevance of the evaluation. Comparative analyses against standalone LSTM and TCN architectures show that the hybrid model achieves the highest predictive accuracy, yielding the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE)values and a coefficient of determination (R²) of 0.9975. These results demonstrate the effectiveness of the proposed hybrid framework in delivering reliable PV power forecasts and supporting improved integration of solar energy into smart grid systems.

Keywords:

solar energy, PV power forecasting, deep learning, LSTM, TCN, hybrid LSTM-TCN model

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References

M. F. Tahir, A. Tzes, and M. Z. Yousaf, "Enhancing PV power forecasting with deep learning and optimizing solar PV project performance with economic viability: A multi-case analysis of 10 MW Masdar project in UAE," Energy Conversion and Management, vol. 311, July 2024, Art. no. 118549. DOI: https://doi.org/10.1016/j.enconman.2024.118549

D. Salman, C. Direkoglu, M. Kusaf, and M. Fahrioglu, "Hybrid deep learning models for time series forecasting of solar power," Neural Computing and Applications, vol. 36, no. 16, pp. 9095–9112, June 2024. DOI: https://doi.org/10.1007/s00521-024-09558-5

H. N. Nguyen, Q. T. Tran, C. T. Ngo, D. D. Nguyen, and V. Q. Tran, "Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms," Plos One, vol. 20, no. 1, Jan. 2025, Art. no. e0315955. DOI: https://doi.org/10.1371/journal.pone.0315955

M. Bousla et al., "Modeling Wind Energy Production Forecasting using Machine Learning: An In-depth Analysis of Wind Farms in Morocco," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 23268–23276, June 2025. DOI: https://doi.org/10.48084/etasr.10296

A. Mellit, A. M. Pavan, E. Ogliari, S. Leva, and V. Lughi, "Advanced Methods for Photovoltaic Output Power Forecasting: A Review," Applied Sciences, vol. 10, no. 2, Jan. 2020, Art. no. 487. DOI: https://doi.org/10.3390/app10020487

T. Ma, F. Li, R. Gao, S. Hu, and W. Ma, "Short-term photovoltaic power forecasting based on a new hybrid deep learning model incorporating transfer learning strategy," Global Energy Interconnection, vol. 7, no. 6, pp. 825–835, Dec. 2024. DOI: https://doi.org/10.1016/j.gloei.2024.11.010

M. S. Hossain and H. Mahmood, "Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast," IEEE Access, vol. 8, pp. 172524–172533, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3024901

K. Wang, X. Qi, and H. Liu, "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, vol. 189, Dec. 2019, Art. no. 116225. DOI: https://doi.org/10.1016/j.energy.2019.116225

X. Xiang, X. Li, Y. Zhang, and J. Hu, "A short-term forecasting method for photovoltaic power generation based on the TCN-ECANet-GRU hybrid model," Scientific Reports, vol. 14, no. 1, Mar. 2024, Art. no. 6744. DOI: https://doi.org/10.1038/s41598-024-56751-6

M. N. Akhter et al., "An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants," Energies, vol. 15, no. 6, Mar. 2022, Art. no. 2243. DOI: https://doi.org/10.3390/en15062243

P. Li, K. Zhou, X. Lu, and S. Yang, "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, vol. 259, Feb. 2020, Art. no. 114216. DOI: https://doi.org/10.1016/j.apenergy.2019.114216

A. Agga, A. Abbou, M. Labbadi, and Y. El Houm, "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, vol. 177, pp. 101–112, Nov. 2021. DOI: https://doi.org/10.1016/j.renene.2021.05.095

Y. Xu, X. Ji, and Z. Zhu, "A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model," Scientific Reports, vol. 15, no. 1, Aug. 2025, Art. no. 30177. DOI: https://doi.org/10.1038/s41598-025-16368-9

A. Rafati, M. Joorabian, E. Mashhour, and H. R. Shaker, "High dimensional very short-term solar power forecasting based on a data-driven heuristic method," Energy, vol. 219, Mar. 2021, Art. no. 119647. DOI: https://doi.org/10.1016/j.energy.2020.119647

A. Lionis, K. Peppas, H. E. Nistazakis, A. Tsigopoulos, K. Cohn, and A. Zagouras, "Using Machine Learning Algorithms for Accurate Received Optical Power Prediction of an FSO Link over a Maritime Environment," Photonics, vol. 8, no. 6, June 2021, Art. no. 212. DOI: https://doi.org/10.3390/photonics8060212

V. H. Wentz, J. N. Maciel, J. J. G. Ledesma, and O. H. A. Junior, "Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models," Energies, vol. 15, no. 7, Mar. 2022, Art. no. 2457. DOI: https://doi.org/10.3390/en15072457

K. Ferkous, M. Guermoui, S. Menakh, A. Bellaour, and T. Boulmaiz, "A novel learning approach for short-term photovoltaic power forecasting - A review and case studies," Engineering Applications of Artificial Intelligence, vol. 133, July 2024, Art. no. 108502. DOI: https://doi.org/10.1016/j.engappai.2024.108502

K. Shivashankaraiah et al., "Developing a Deep Learning and Reliable Optimization Techniques for Solar Photovoltaic Power Prediction," Electric Power Components and Systems, pp. 1–15, Feb. 2024. DOI: https://doi.org/10.1080/15325008.2024.2317369

S. T. Asiedu, F. K. A. Nyarko, S. Boahen, F. B. Effah, and B. A. Asaaga, "Machine learning forecasting of solar PV production using single and hybrid models over different time horizons," Heliyon, vol. 10, no. 7, Apr. 2024, Art. no. e28898. DOI: https://doi.org/10.1016/j.heliyon.2024.e28898

M. Abdelsattar, M. A. Azim, A. AbdelMoety, and A. Emad-Eldeen, "Comparative analysis of deep learning architectures in solar power prediction," Scientific Reports, vol. 15, no. 1, Aug. 2025, Art. no. 31729. DOI: https://doi.org/10.1038/s41598-025-14908-x

A. P. Renold, N. Sinha, and X.-Z. Gao, "Hybrid machine learning approach for improved short-term PV power forecasting accuracy," Results in Engineering, vol. 28, Dec. 2025, Art. no. 107374. DOI: https://doi.org/10.1016/j.rineng.2025.107374

Y. Ledmaoui, A. El Maghraoui, M. El Aroussi, R. Saadane, A. Chebak, and A. Chehri, "Forecasting solar energy production: A comparative study of machine learning algorithms," Energy Reports, vol. 10, pp. 1004–1012, Nov. 2023. DOI: https://doi.org/10.1016/j.egyr.2023.07.042

T. Negash, N. Weldemikael, M. Ghebregziabiher, Y. Tedla, S. István, and F. István, "Addressing photovoltaic (PV) forecasting challenges: Satellite-driven data models for predicting actual PV generation using hybrid (LSTM-GRU) model," Energy Reports, vol. 14, pp. 2141–2156, Dec. 2025. DOI: https://doi.org/10.1016/j.egyr.2025.08.034

M. Zhang, Y. Han, C. Wang, P. Yang, C. Wang, and A. S. Zalhaf, "Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data," Applied Energy, vol. 375, Dec. 2024, Art. no. 124085. DOI: https://doi.org/10.1016/j.apenergy.2024.124085

R. Elmousaid, N. Drioui, R. Elgouri, H. Agueny, and Y. Adnani, "Ultra-short-term global horizontal irradiance forecasting based on a novel and hybrid GRU-TCN model," Results in Engineering, vol. 23, Sept. 2024, Art. no. 102817. DOI: https://doi.org/10.1016/j.rineng.2024.102817

K. Olcay, S. Gíray Tunca, and M. Aríf Özgür, "Forecasting and Performance Analysis of Energy Production in Solar Power Plants Using Long Short-Term Memory (LSTM) and Random Forest Models," IEEE Access, vol. 12, pp. 103299–103312, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3432574

M. Elsaraiti and A. Merabet, "Solar Power Forecasting Using Deep Learning Techniques," IEEE Access, vol. 10, pp. 31692–31698, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3160484

G. Li, S. Xie, B. Wang, J. Xin, Y. Li, and S. Du, "Photovoltaic Power Forecasting With a Hybrid Deep Learning Approach," IEEE Access, vol. 8, pp. 175871–175880, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3025860

T. Limouni, R. Yaagoubi, K. Bouziane, K. Guissi, and E. H. Baali, "Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model," Renewable Energy, vol. 205, pp. 1010–1024, Mar. 2023. DOI: https://doi.org/10.1016/j.renene.2023.01.118

N. B. Sushmi and D. Subbulekshmi, "Real-time ultra short-term irradiance forecasting using a novel R-GRU model for optimizing PV controller dynamics," Results in Engineering, vol. 26, June 2025, Art. no. 105046. DOI: https://doi.org/10.1016/j.rineng.2025.105046

S. Riad, N. Bekkioui, M. Simo-Tagne, N. M. Chinenye, and H. Ez-Zahraouy, "Artificial Intelligence Prediction Analysis of Daily Power Photovoltaic Bifacial Modules in Two Moroccan Cities," Sustainability, vol. 17, no. 15, July 2025, Art. no. 6900. DOI: https://doi.org/10.3390/su17156900

B. U. D. Abdullah, S. A. Khanday, N. U. Islam, S. Lata, H. Fatima, and S. H. Nengroo, "Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation," Energies, vol. 17, no. 7, Mar. 2024, Art. no. 1564. DOI: https://doi.org/10.3390/en17071564

Y. Ledmaoui et al., "Enhancing Solar Power Efficiency: Smart Metering and ANN-Based Production Forecasting," Computers, vol. 13, no. 9, Sept. 2024, Art. no. 235. DOI: https://doi.org/10.3390/computers13090235

R. Guanoluisa, D. Arcos-Aviles, M. Flores-Calero, W. Martinez, and F. Guinjoan, "Photovoltaic Power Forecast Using Deep Learning Techniques with Hyperparameters Based on Bayesian Optimization: A Case Study in the Galapagos Islands," Sustainability, vol. 15, no. 16, Aug. 2023, Art. no. 12151. DOI: https://doi.org/10.3390/su151612151