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Yaqun Jiang

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EAAI Journal 2024 Journal Article

Load forecasting model considering dynamic coupling relationships using structured dynamic-inner latent variables and broad learning system

  • Ziwen Gu
  • Yatao Shen
  • Zijian Wang
  • Jiayi Qiu
  • Wenmei Li
  • Chun Huang
  • Yaqun Jiang
  • Peng Li

Integrated energy systems (IES) can effectively regulate and optimize dynamic loads by utilizing load forecasting, which intelligently manages energy scheduling. Nevertheless, the insufficient attention given by existing research to loads with multiple frequency scales and dynamic coupling relationships in the IES may lead to a reduced accuracy in load forecasting. To address this issue, a novel load forecasting model using structured dynamic-inner latent variables and broad learning system (SDiLV-BLS) is proposed. Firstly, the dynamic coupling reconstruction method is introduced to restructure the data of various load types into multimodal data with coupling relationships across multiple frequency scales, serving as input for the forecasting model. Then, the structured dynamic-inner latent variables (SDiLV) decouple the multimodal data to obtain dynamic features that have regression properties and simplify the complexity of the input features for the forecasting model. Finally, the broad learning system (BLS) is employed to reveal the structured geometric patterns in the extracted features, which achieves accurate load forecasting. Case studies show that by employing the coupling feature selection strategy in SDiLV-BLS, there is an improvement in forecasting accuracy compared to the strategy of selecting only load features of the same type and the strategy of selecting all load type features. Furthermore, by selecting coupling features, the average root mean square error (RMSE) and mean absolute error (MAE) of SDiLV-BLS are reduced by 21. 66% and 22. 06%, respectively, compared to the original BLS in multi-step ahead forecasting.

Details DOI

EAAI Journal 2024 Journal Article

Wind speed prediction utilizing dynamic spectral regression broad learning system coupled with multimodal information

  • Ziwen Gu
  • Yatao Shen
  • Zijian Wang
  • Jiayi Qiu
  • Wenmei Li
  • Chun Huang
  • Yaqun Jiang

As the integration of wind energy into the power system increases, accurate wind speed prediction becomes crucial to ensure the reliable and economically efficient operation of the grid. The non-stationary, chaotic, and nonlinear characteristics of wind speed pose significant challenges for prediction models in uncovering the dynamic evolution process. To address these challenges, we proposed a wind speed prediction method based on the dynamic spectral regression broad learning system coupled with multimodal information (DSR-BLS). Firstly, we proposed a frequency density clustering-based mode decomposition (FDCMD) algorithm, which automatically transforms the non-stationary wind speed into multiple relatively stationary modal components. Next, we proposed the dynamic spectral regression (DSR) algorithm, which is based on dynamic-inner latent variable modeling and spectral regression. DSR can extract features and reconstruct the dynamic characteristics of wind speed through non-uniform embedding in the phase space. Finally, DSR-BLS is proposed to enhance the deterministic point prediction accuracy of the original BLS by utilizing multimodal features and dynamic features. The experiments show that DSR-BLS outperforms the comparative prediction methods in multi-step ahead prediction results.

Details DOI
v2026.09.13