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SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting

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  • Korkmaz, Deniz

Abstract

Photovoltaic (PV) power generation has high uncertainties due to the randomness and imbalance nature of solar energy and meteorological parameters. Hence, accurate PV power forecasts are essential in the operation of PV power plants (PVPP) for short-term dispatches and power generation schedules. In this study, a novel convolutional neural network (CNN) model, namely SolarNet, is proposed for short-term PV output power forecasting under different weather conditions and seasons. The proposed CNN model is designed as a parallel pooling structure to increase the forecasting performance. This structure consists of max-pooling and average-pooling blocks. The input parameters are the measured historical solar radiation, temperature, humidity, and active power data. The power data is decomposed into sub-components with the variational mode decomposition method and a data preprocessing and reconstruction process is utilized to obtain deep input feature maps. After input parameters are converted to hue-saturation-value (HSV) color space, the subsets feed to the input of the network. The experimental studies are performed with a case study using a 23.40 kW PVPP dataset from the Desert Knowledge Australia Solar Centre. The design CNN model is also compared with benchmark deep learning methods. In the experiments, the average correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the proposed method for 1-h different weather conditions are achieved as 0.9871, 0.3090, and 0.1750, respectively. The experimental results show that the proposed deep forecasting method has higher accuracy and stability in short-term PV power forecasting and outperforms the other deep learning methods.

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  • Korkmaz, Deniz, 2021. "SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:appene:v:300:y:2021:i:c:s0306261921008072
    DOI: 10.1016/j.apenergy.2021.117410
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    16. Khan, Zulfiqar Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2023. "Dual stream network with attention mechanism for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 338(C).
    17. Yang, Mao & Zhao, Meng & Huang, Dawei & Su, Xin, 2022. "A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder," Renewable Energy, Elsevier, vol. 194(C), pages 659-673.
    18. Li, Ke & Mu, Yuchen & Yang, Fan & Wang, Haiyang & Yan, Yi & Zhang, Chenghui, 2023. "A novel short-term multi-energy load forecasting method for integrated energy system based on feature separation-fusion technology and improved CNN," Applied Energy, Elsevier, vol. 351(C).
    19. Zaohui Kang & Jizhong Xue & Chun Sing Lai & Yu Wang & Haoliang Yuan & Fangyuan Xu, 2023. "Vision Transformer-Based Photovoltaic Prediction Model," Energies, MDPI, vol. 16(12), pages 1-14, June.
    20. Yin, Linfei & Cao, Xinghui & Liu, Dongduan, 2023. "Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 332(C).
    21. Keyong Hu & Zheyi Fu & Chunyuan Lang & Wenjuan Li & Qin Tao & Ben Wang, 2024. "Short-Term Photovoltaic Power Generation Prediction Based on Copula Function and CNN-CosAttention-Transformer," Sustainability, MDPI, vol. 16(14), pages 1-18, July.
    22. liu, Qian & li, Yulin & jiang, Hang & chen, Yilin & zhang, Jiang, 2024. "Short-term photovoltaic power forecasting based on multiple mode decomposition and parallel bidirectional long short term combined with convolutional neural networks," Energy, Elsevier, vol. 286(C).
    23. Kim, Jimin & Obregon, Josue & Park, Hoonseok & Jung, Jae-Yoon, 2024. "Multi-step photovoltaic power forecasting using transformer and recurrent neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    24. Liu, Jiefeng & Zhang, Zhenhao & Fan, Xianhao & Zhang, Yiyi & Wang, Jiaqi & Zhou, Ke & Liang, Shuo & Yu, Xiaoyong & Zhang, Wei, 2022. "Power system load forecasting using mobility optimization and multi-task learning in COVID-19," Applied Energy, Elsevier, vol. 310(C).

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