Journal article
Hybrid Deep Learning Models for Power Output Forecasting of Grid-Connected Solar PV Systems: A Monocrystalline and Polycrystalline PV Panel Analysis
International journal of energy research, v 2025(1), 9925615
01 Jan 2025
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Increasing the use of renewable energy, particularly photovoltaic (PV) systems, is essential for mitigating climate change. However, the intermittent nature of PV power generation creates challenges in accurately forecasting and managing electricity supply within grid systems. This study proposes a hybrid deep learning (DL) model combining improved harmony search (IHS) optimization, convolutional neural networks (CNNs), and long short-term memory (LSTM) networks (IHS-CNN-LSTM) for forecasting the 15-min power output of grid-connected monocrystalline and polycrystalline PV systems. The model uses 14 input features obtained from numerical weather prediction (NWP) and local measurement data (LMD), with data sourced from the Public Photovoltaic Output Dataset (PVOD) covering June 2018 to June 2019 in Hebei Province, China. Comparative evaluations against genetic algorithm-based (GA-CNN-LSTM), differential evolution-based (DE-CNN-LSTM), and conventional CNN-LSTM models showed that the IHS-CNN-LSTM provided superior forecasting accuracy. Specifically, the proposed model achieved reductions in root mean square error (RMSE) of 3.7% for polycrystalline and 1.8% for monocrystalline PV systems, and reductions in mean absolute error (MAE) of 2.6% and 1.2%, respectively, along with high R2 values of 98% and 99%. The results confirm the effectiveness and accuracy of the proposed hybrid approach for PV power output forecasting.
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Details
- Title
- Hybrid Deep Learning Models for Power Output Forecasting of Grid-Connected Solar PV Systems: A Monocrystalline and Polycrystalline PV Panel Analysis
- Creators
- Abdulhameed Babatunde Owolabi - Kyungpook National UniversityAbdullahi Yahaya - Kyungpook National UniversityAbdulfatai Olatunji Yakub - Kyungpook National UniversityNoel Ngando Same - Kyungpook National UniversityMohammad Amir - University of LiverpoolMohammad Awwal Adeshina - Daegu Gyeongbuk Institute of Science and TechnologyDongjun Suh (Corresponding Author) - Kyungpook National University
- Publication Details
- International journal of energy research, v 2025(1), 9925615
- Publisher
- Wiley
- Number of pages
- 22
- Grant note
- Scientific Data Bank National Research Foundation of Korea
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:001547812700001
- Scopus ID
- 2-s2.0-105012884004
- Other Identifier
- 991022197422704721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Energy & Fuels
- Nuclear Science & Technology