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A hybrid correction framework using disentangled seasonal-trend representations and MoE for NWP solar irradiance forecast

Author

Listed:
  • Dou, Weijing
  • Wang, Kai
  • Shan, Shuo
  • Zhang, Kanjian
  • Wei, Haikun
  • Sreeram, Victor

Abstract

Day-ahead solar irradiance forecast holds important value for optimizing energy utilization within the power system and ensuring stable grid scheduling. The forecast outputs of numerical weather prediction (NWP) are widely acknowledged as one of the indispensable data sources for day-ahead solar irradiance forecast tasks. In previous studies, post-processing methods have generally been employed as correction models to enhance the accuracy of NWP solar irradiance forecasts. However, irradiance sequences contain complex mixed patterns and exhibit various seasonal periodic differences. Based on the analysis of NWP global horizontal irradiance (GHI) error characteristics in this study, errors in NWP GHI forecasts also show obvious seasonal variations. Given these issues, it is challenging for a single correction model to achieve good correction performance and strong seasonal robustness. Therefore, this paper proposes a hybrid model comprising representation learning module, feature sparse activation module, and encoder-decoder-based correction module to address the aforementioned problems. A contrastive-learning-based representation learning module named CoST is introduced to learn disentangled seasonal features and trend features of irradiance sequences. A learnable mixture-of-experts (MoE) layer is adopted to sparsely activate the seasonal-trend features that contribute more to improving correction accuracy. The encoder-decoder-based correction module takes the sparsely activated seasonal-trend features as inputs, achieving the final corrected NWP GHI forecasts. The correction performance of the proposed method was validated on both publicly available datasets and actual field dataset. The results for various datasets show that our proposed CoST-MoELSTM model achieves the highest improvement for NWP forecasts, with increases of 29.82 %, 36.54 %, and 26.58 %. Additionally, we conducted a detailed analysis of the correction performance of CoST-MoELSTM across different seasons, indicating its superior seasonal robustness.

Suggested Citation

  • Dou, Weijing & Wang, Kai & Shan, Shuo & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2025. "A hybrid correction framework using disentangled seasonal-trend representations and MoE for NWP solar irradiance forecast," Applied Energy, Elsevier, vol. 397(C).
    • Handle: RePEc:eee:appene:v:397:y:2025:i:c:s0306261925010256
    DOI: 10.1016/j.apenergy.2025.126295
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    1. Pedregal, Diego J. & Trapero, Juan R., 2021. "Adjusted combination of moving averages: A forecasting system for medium-term solar irradiance," Applied Energy, Elsevier, vol. 298(C).
    2. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    3. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    4. Niu, Zhewen & Han, Xiaoqing & Zhang, Dongxia & Wu, Yuxiang & Lan, Songyan, 2024. "Interpretable wind power forecasting combining seasonal-trend representations learning with temporal fusion transformers architecture," Energy, Elsevier, vol. 306(C).
    5. Lan, Hai & Yin, He & Hong, Ying-Yi & Wen, Shuli & Yu, David C. & Cheng, Peng, 2018. "Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route," Applied Energy, Elsevier, vol. 211(C), pages 15-27.
    6. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    7. Zhao, He & Huang, Xiaoqiao & Xiao, Zenan & Shi, Haoyuan & Li, Chengli & Tai, Yonghang, 2024. "Week-ahead hourly solar irradiation forecasting method based on ICEEMDAN and TimesNet networks," Renewable Energy, Elsevier, vol. 220(C).
    8. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    9. Wen, Haoran & Du, Yang & Chen, Xiaoyang & Lim, Eng Gee & Wen, Huiqing & Yan, Ke, 2023. "A regional solar forecasting approach using generative adversarial networks with solar irradiance maps," Renewable Energy, Elsevier, vol. 216(C).
    10. Huang, Chao & Bensoussan, Alain & Edesess, Michael & Tsui, Kwok L., 2016. "Improvement in artificial neural network-based estimation of grid connected photovoltaic power output," Renewable Energy, Elsevier, vol. 97(C), pages 838-848.
    11. Huang, Xiaoqiao & Liu, Jun & Xu, Shaozhen & Li, Chengli & Li, Qiong & Tai, Yonghang, 2023. "A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting," Energy, Elsevier, vol. 272(C).
    12. Sharma, Amandeep & Kakkar, Ajay, 2018. "Forecasting daily global solar irradiance generation using machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2254-2269.
    13. Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting," Applied Energy, Elsevier, vol. 342(C).
    14. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    15. Mayer, Martin János & Yang, Dazhi & Szintai, Balázs, 2023. "Comparing global and regional downscaled NWP models for irradiance and photovoltaic power forecasting: ECMWF versus AROME," Applied Energy, Elsevier, vol. 352(C).
    16. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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