{"id":2571,"date":"2026-04-06T14:41:42","date_gmt":"2026-04-06T06:41:42","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=2571"},"modified":"2026-05-24T23:19:29","modified_gmt":"2026-05-24T15:19:29","slug":"an-effective-and-efficient-renewable-energy-generation-forecasting-via-meteorological-assistance","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=an-effective-and-efficient-renewable-energy-generation-forecasting-via-meteorological-assistance","title":{"rendered":"An Effective and Efficient Renewable Energy Generation Forecasting via Meteorological Assistance"},"content":{"rendered":"\n
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<\/i> 2025<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 28, Issue 7<\/a><\/p>\n<\/div>\n\n\n\n

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Zengyao Tian1,2<\/sup>, Li Lv1<\/sup><\/i><\/a>, Wenchen Deng1<\/sup>, and Zhikui Chen3<\/sup><\/p>\n\n\n\n

1<\/sup>Shenyang Institute of Computing Technology Chinese Academy of Sciences, Shenyang, China<\/p>\n\n\n\n

2<\/sup>University of Chinese Academy of Sciences, Beijing, China<\/p>\n\n\n\n

3<\/sup>School of Software Technology, Dalian University of Technology, Dalian, Chin<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0September 5, 2024
Accepted:\u00a0September 29, 2024
Publication Date:\u00a0April 6, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Overview of the SPM-REPS architecture for renewable energy generation forecasting: integration of meteorological and power signals with time-correlation and feature-correlation learning mechanisms.<\/p>\n\n\n\n

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<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202507_28(7).0020<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Accurate signal pattern mining of renewable energy generation forecasting (REGF) is important to the days ahead power scheduling of renewable energy power systems. Despite achieving excellent performance with current methods, two issues still persist. (1) They solely utilize historical meteorological signal data to assist in power signal forecasting and neglect valuable information in future information of meteorological signals, consequently limiting their performance. (2) They pursue predictive performance by designing complex architectures and mechanisms, which may lead to insufficient model generalization. To this end, an effective and efficient MLP architecture is proposed to mine REGF signal patterns in renewable energy power systems (SPM-REPS), which contains power signal forecast architecture and meteorological signal forecast architecture. Two architectures seamlessly collaborate in forecasting power generation patterns, which achieves better performance. Meanwhile, time-correlation and feature-correlation strategies are devised within MLP networks to capture both intra-sequence and inter-sequence correlations of signal variables like transformer- and RNN based methods. Furthermore, a theoretical analysis of linear architecture is given to prove the progressiveness of SPM-REPS. Finally, numerous experiments, conducted on common datasets (CSG-PV and CSG-wind) from Chinese State Grid, demonstrate SPM-REPS sets a new benchmark in mining REGF signal patterns of REPS.<\/p>\n\n\n\n

Keywords:\u00a0Power forecasting; meteorological assistance; time- and feature-correlations<\/em><\/p>\n\n\n\n

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  1. [1] S. Wang, J. Shi, W. Yang, and Q. Yin, (2024) \u201cHigh and low frequency wind power prediction based on Transformer and BiGRU-Attention\u201d Energy 288: 129753. DOI: doi.org\/10.1016\/j.energy.2023.129753.<\/li>\n
  2. [2] J. Yu, J. Pu, Y. Cheng, R. Feng, and Y. Shan, (2023) \u201cLearning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization\u201d IEEE Transactions on Multimedia: DOI: 10.1109\/TMM.2023.3303690.<\/li>\n
  3. [3] P. Li, J. Gao, J. Zhang, S. Jin, and Z. Chen, (2022) \u201cDeep Reinforcement Clustering\u201d IEEE Transactions on Multimedia 25: 8183\u20138193. DOI: 10.1109\/TMM.2022.3233249.<\/li>\n
  4. [4] J. Gao, M. Liu, P. Li, J. Zhang, and Z. Chen, (2023) \u201cDeep multiview adaptive clustering with semantic invariance\u201d IEEE Transactions on Neural Networks and Learning Systems: DOI: 10.1109\/TNNLS.2023.3265699.<\/li>\n
  5. [5] X. Wang, J. Li, L. Shao, H. Liu, L. Ren, and L. Zhu, (2023) \u201cShort-term wind power prediction by an extreme learning machine based on an improved hunter\u2013prey optimization algorithm\u201d sustainability 15(2): 991. DOI: 10.1030\/su15020991.<\/li>\n
  6. [6] Y.-J. Ma and M.-Y. Zhai, (2019) \u201cA dual-step integrated machine learning model for 24h-ahead wind energy generation prediction based on actual measurement data and environmental factors\u201d Applied Sciences 9(10): 2125. DOI: 10.3390\/app9102125.<\/li>\n
  7. [7] K. Kim and J. Hur, (2019) \u201cWeighting factor selection of the ensemble model for improving forecast accuracy of photovoltaic generating resources\u201d Energies 12(17): 3315. DOI: 10.3390\/en12173315.<\/li>\n
  8. [8] Y. Zhou, N. Zhou, L. Gong, and M. Jiang, (2020) \u201cPrediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine\u201d Energy 204: 117894. DOI: 10.1016\/j.energy.2020.117894.<\/li>\n
  9. [9] L.-L. Li, S.-Y. Wen, M.-L. Tseng, and C.-S. Wang, (2019) \u201cRenewable energy prediction: A novel short-term prediction model of photovoltaic output power\u201d Journal of Cleaner Production 228: 359\u2013375. DOI: 10.1016\/j.jclepro.2019.04.331.<\/li>\n
  10. [10] Y. Han, N. Wang, M. Ma, H. Zhou, S. Dai, and H. Zhu, (2019) \u201cA PV power interval forecasting based on seasonal model and nonparametric estimation algorithm\u201d Solar Energy 184: 515\u2013526. DOI: 10.1016\/j.solener.2019.04.025.<\/li>\n
  11. [11] D. Widodo, N. Iksan, E. Udayanti, et al. \u201cRenewable energy power generation forecasting using deep learning method\u201d. In: IOP Conference Series: Earth and Environmental Science. 700. 1. 2021, 012026. DOI: 10.1088\/1755-1315\/700\/1\/012026.<\/li>\n
  12. [12] J. Gao, P. Li, A. A. Laghari, G. Srivastava, T. R. Gadekallu, S. Abbas, and J. Zhang, (2024) \u201cIncomplete multiview clustering via semidiscrete optimal transport for multimedia data mining in IoT\u201d ACM Transactions on Multimedia Computing, Communications and Applications 20(6): 1\u201320. DOI: 10.1145\/3625548.<\/li>\n
  13. [13] Z.-H. Liu, C.-T. Wang, H.-L. Wei, B. Zeng, M. Li, and X.-P. Song, (2024) \u201cA wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data\u201d Expert Systems with Applications 247: 123237. DOI: 10.1016\/j.eswa.2024.123237.<\/li>\n
  14. [14] H. Zang, L. Cheng, T. Ding, K. W. Cheung, Z. Liang, Z. Wei, and G. Sun, (2018) \u201cHybrid method for short-term photovoltaic power forecasting based on deep convolutional neural network\u201d IET Generation, Transmission & Distribution 12(20): 4557\u20134567. DOI: 10.1049\/iet-gtd.2018.5847.<\/li>\n
  15. [15] Y. Chen and J. Xu, (2022) \u201cSolar and wind power data from the Chinese state grid renewable energy generation forecasting competition\u201d Scientific Data 9(1): 577. DOI: 10.1038\/s41597-022-01696-6.<\/li>\n
  16. [16] H. Wu, J. Xu, J. Wang, and M. Long, (2021) \u201cAutoformer: Decomposition transformers with auto-correlation for long-term series forecasting\u201d Advances in neural information processing systems 34: 22419\u201322430.<\/li>\n
  17. [17] B. Lim, S. \u00d6. Ar\u0131k, N. Loeff, and T. Pfister, (2021) \u201cTemporal fusion transformers for interpretable multi-horizon time series forecasting\u201d International Journal of Forecasting 37(4): 1748\u20131764. DOI: 10.1016\/j.ijforecast.2021.03.012.<\/li>\n
  18. [18] T. Zhou, Z. Ma, Q. Wen, X. Wang, L. Sun, and R. Jin. \u201cFedformer: Frequency enhanced decomposed transformer for long-term series forecasting\u201d. In: International conference on machine learning. 2022, 27268\u201327286.<\/li>\n
  19. [19] H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang. \u201cInformer: Beyond efficient transformer for long sequence time-series forecasting\u201d. In: Proceedings of the AAAI conference on artificial intelligence. 35. 12. 2021, 11106\u201311115. DOI: 10.1609\/aaai.v35i12.17325.<\/li>\n
  20. [20] A. F. Mirza, M. Mansoor, M. Usman, and Q. Ling, (2023) \u201cHybrid Inception-embedded deep neural network ResNet for short and medium-term PV-Wind forecasting\u201d Energy Conversion and Management 294: 117574. DOI: 10.1016\/j.enconman.2023.117574.<\/li>\n
  21. [21] A. Agga, A. Abbou, M. Labbadi, and Y. El Houm, (2021) \u201cShort-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models\u201d Renewable Energy 177: 101\u2013112. DOI: 10.1016\/j.renene.2021.05.095.<\/li>\n
  22. [22] L. Yin and M. Zhao, (2023) \u201cInception-embedded attention memory fully-connected network for short-term wind power prediction\u201d Applied Soft Computing 141: 110279. DOI: 10.1016\/j.asoc.2023.110279.<\/li>\n
  23. [23] Y.-M. Zhang and H. Wang, (2023) \u201cMulti-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting\u201d Energy 278: 127865. DOI: 10.1016\/j.energy.2023.127865.<\/li>\n<\/ol>\n<\/div>\n\n\n\n

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