Solar Power Generation Prediction Using LSTM Deep Learning Algorithm in Ningxia Province, China
DOI:
https://doi.org/10.55674/ias.v14i3.262329Keywords:
Photovoltaic power generation, Deep learning algorithm, Long short-term memory, Power generation predictionAbstract
The rapid expansion of photovoltaic (PV) power generation faces significant challenges due to the intermittent and stochastic characteristics of solar energy, which affect grid stability and energy management. Accurate forecasting of PV power output is crucial for optimizing grid operations and supporting the transition to clean energy. This paper proposes a deep learning approach based on Long Short-Term Memory (LSTM) networks to predict PV power generation in Ningxia Province, China. The model leverages historical power and meteorological data, which undergo comprehensive preprocessing, including outlier removal, normalization, and feature correlation analysis. The experiment is based on collecting data at 15-minute intervals, totaling 35,000 samples from a 1 MW photovoltaic power station in Ningxia for the entire year of 2023. The data include seven characteristic dimensions such as irradiance, temperature, and humidity. Comparative experiments involving Support Vector Machine (SVM), Convolutional Neural Network (CNN), and LSTM demonstrate that LSTM outperforms other methods with superior accuracy and robustness, achieving a coefficient of determination (R²) of 0.9927. The results confirm LSTM's effectiveness in capturing temporal dependencies and nonlinear patterns in PV power data. This study provides valuable insights for enhancing photovoltaic grid integration and advancing intelligent power systems.
References
L. Xingyun, Research on photovoltaic power generation time series prediction method, Tianjin University of Science and Technology. 15(2) (2023).
C. Yang, Photovoltaic power prediction based on deep learning, Tianjin University of Science and Technology. 8(4) (2023).
Z. Manguo, Research on photovoltaic power generation prediction based on deep learning, Jiangxi University of Science and Technology. 12(1) (2022).
N.L.M. Jailani, J.K. Dhanasegaran, G. Alkawsi, A.A. Alkahtani, C.C. Phing, Y. Baashar, L.F. Capretz, A.Q. Al-Shetwi, S.K. Tiong, Investigating the Power of LSTM-Based Models in Solar Energy Forecasting, Processes. (11) (2023) 1382.
D.K. Dhaked, S. Dadhich, D. Birla, Power output forecasting of solar photovoltaic plant using LSTM, Green Energy and Intelligent Transportation. 2 (2023) 100113.
L. Wang, M. Mao, J. Xie, Z. Liao, H. Zhang, H. Li, Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model, Energy. 262 (2023) 125592.
F. Lei, Z. Jinsheng, L Haiyan, Analysis of solar energy resource characteristics in Taiyangshan area of Ningxia based on field measurements, Acta Energiae Solaris Sinica. 41(12) (2020) 146 − 153.
S. Yinchuan, B. Yongqing, Z. Hejiang, A preliminary study on the correction of localized WRF radiation forecast and photovoltaic power generation prediction method in Ningxia, Chinese Journal of Desertification. 32(06) (2012) 1738 − 1742.
W. Lei, J. Ning, Y. Guangliang, Research on the ability of Ningxia power system to accept new energy, Power System Technology. 34(11) (2010) 176 − 181.
G. Pengjiang, L. Yi, Analysis of output characteristics of photovoltaic power stations in Ningxia, Electrical Automation. 38(2) (2016) 33 − 36.
H. Jiafeng, F. Shuanglei, D. Maosheng, Wind-solar integrated power prediction system for Ningxia power grid, Ningxia Electric Power, (2011).
W. Yingming, A brief analysis of the paths and measures for building solar photovoltaic integration in Ningxia, Architecture. 17(1) (2022) 52 − 55.
C. Chen, S. Hua, C. Wang, Assessment of different deep learning methods of power generation forecasting for solar PV system, Applied Sciences. 12(15) (2022) 7529.
J. Schmidhuber, S. Hochreiter, Long short-term memory, Neural Computation. 9 (1997) 1735 – 1780.
F. Gers, F. Cummins, Learning to forget: Continual prediction with LSTM. Neural Computation. 12 (2000) 2451 – 2471.
S. Singh, A. Gupta, R. Chandel, S. Tajjour, Review of deep learning techniques for power generation prediction of industrial solar photovoltaic plants, Solar Compass. 8 (2023) 100061.
L. Zhiqin, D. Jianqiang, N. Bin, X. Wangping, H. Canyi, L. Huan, A review of feature selection methods, Computer Engineering and Applications. 55(24) (2019) 10 − 19.
Z, Xiangyu, MLP, CNN, LSTM and Hybrid SVM for stock index forecasting task to indu and FTSE 100, (2020). Available at SSRN 3644034 .
Z. Jinsong, R. Verschae, S. Nobuhara, Deep photovoltaic nowcasting, Solar Energy. 176 (2018) 267 − 276.
T. Hattiya, K. Dittakan, S. Musikasuwan, Diabetic Retinopathy Detection using Convolutional Neural Network: A Comparative Study on Different Architectures, Engineering Access. 7(1) 50 – 60.
B. Mingliang, X. Zhao, Z. Long, J. Liu, D. Yu, Short-term probabilistic photovoltaic power forecast based on deep convolutional long short-term memory network and kernel density estimation. (2021) 2107.01343.
E. Michael, N. M. Mishra, S. Hasan, A. Al-Durra, Short-term solar power predicting model based on multi-step CNN stacked LSTM technique, Energies. 15(6) (2022) 2150.
A. Musaed, K. Aurangzeb, S. Haider, Hybrid CNN-LSTM model for short-term individual household load forecasting, IEEE Access. 8 (2020): 180544 − 180557.
J. Tobias, A. Klein, S. Falkner, F. Hutter, Bayesian optimization with robust Bayesian neural networks, Advances in neural information processing systems. 29 (2016).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Indochina Applied Science
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
