Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Lakhdar Nadjib Boucetta (), Youssouf Amrane, Aissa Chouder, Saliha Arezki and Sofiane Kichou ()
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Lakhdar Nadjib Boucetta: LSEI Laboratory, Department of Electrical Engineering, University of Science and Technology Houari Boumediene, Bab Ezzouar 16111, Algeria
Youssouf Amrane: LSEI Laboratory, Department of Electrical Engineering, University of Science and Technology Houari Boumediene, Bab Ezzouar 16111, Algeria
Aissa Chouder: Laboratory of Electrical Engineering (LGE), Electrical Engineering Department, University of M’sila, P.O. Box 166 Ichebilia, M’Sila 28000, Algeria
Saliha Arezki: LSEI Laboratory, Department of Electrical Engineering, University of Science and Technology Houari Boumediene, Bab Ezzouar 16111, Algeria
Sofiane Kichou: Czech Technical University in Prague, University Centre for Energy Efficient Buildings, 1024 Třinecká St., 27343 Buštěhrad, Czech Republic

Energies, 2024, vol. 17, issue 7, 1-21

Abstract: Renewable energies have become pivotal in the global energy landscape. Their adoption is crucial for phasing out fossil fuels and promoting environmentally friendly energy solutions. In recent years, the energy management system (EMS) concept has emerged to manage the power grid. EMS optimizes electric grid operations through advanced metering, automation, and communication technologies. A critical component of EMS is power forecasting, which facilitates precise energy grid scheduling. This research paper introduces a deep learning hybrid model employing convolutional neural network–long short-term memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting. The proposed method integrates the variational mode decomposition (VMD) algorithm with the CNN-LSTM model to predict PV power output from a solar farm in Boussada, Algeria, spanning 1 January 2019, to 31 December 2020. The performance of the developed model is benchmarked against other deep learning models across various time horizons (15, 30, and 60 min): variational mode decomposition–convolutional neural network (VMD-CNN), variational mode decomposition–long short-term memory (VMD-LSTM), and convolutional neural network–long short-term memory (CNN-LSTM), which provide a comprehensive evaluation. Our findings demonstrate that the developed model outperforms other methods, offering promising results in solar power forecasting. This research contributes to the primary goal of enhancing EMS by providing accurate solar energy forecasts.

Keywords: solar energy; energy management system (EMS); solar power forecasting; deep learning (DL); convolutional neural network–long short-term memory (CNN-LSTM); variational mode decomposition (VMD) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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