Robrini, Ferial El, Amrouche, Badia, Cali, Umit orcid.org/0000-0002-6402-0479 et al. (1 more author) (2025) Assessment of machine and deep learning models integrated with variational mode decomposition for photovoltaic power forecasting using real-world data from the semi-arid region of Djelfa, Algeria. Energy Conversion and Management: X. 101108. ISSN 2590-1745
Abstract
Accurate photovoltaic power forecasting is essential for grid stability and efficient energy management. While Deep Learning (DL) and Machine Learning (ML) models are widely used, the extent to which each can be effectively leveraged remains an open question. This study thoroughly compares several ML and DL models, applied to both short-term (30-minute) and medium-term (3-hour) horizon. The research is built upon real-world data from a 53 MW PV plant located in Algeria, offering practical insights under realistic conditions. Another critical element of the study is the incorporation of Variational Mode Decomposition (VMD) for feature processing, which mainly enhances information extraction. The study also includes a monthly performance analysis investigating the climatological variability on forecasting accuracy. Among standalone models, CNN performs best with an nMAE of 2.9 %, nRMSE of 5.45 %, and R2 of 0.9678 at 30 min ahead, and nMAE of 3.15 %, RMSE of 4.15 %, and R2 of 0.9839 at 3 h forecasting. When combined with VMD, ML models, particularly ANN, SVM, and Random Forest frequently outperform DL-VMD counterparts. For instance, ANN achieves an nMAE of 1.08 %, nRMSE of 1.89 %, and R2 of 0.9961 at 30 min, and maintains excellent accuracy at 3 h with nMAE of 1.1 %, nRMSE of 1.4 %, and R2 of 0.9982. Collectively, this research serves as a reference for a multidimensional evaluation of forecasting performance. The analysis highlights the importance of selecting appropriate models and preprocessing techniques in PV power forecasting, tailored to location and climatological conditions, contributing to effectively addressing abrupt fluctuations and facilitating large-scale integration.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Publisher Copyright: © 2025 The Author(s) |
Keywords: | Deep learning,Grid-connected photovoltaic power plant,Long short-term memory,Prediction |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 02 Jul 2025 09:50 |
Last Modified: | 02 Jul 2025 09:50 |
Published Version: | https://doi.org/10.1016/j.ecmx.2025.101108 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.ecmx.2025.101108 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:228639 |
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Filename: 1-s2.0-S2590174525002405-main.pdf
Description: Assessment of machine and deep learning models integrated with variational mode decomposition for photovoltaic power forecasting using real-world data from the semi-arid region of Djelfa, Algeria
Licence: CC-BY 2.5