• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (9): 104-116.doi: 10.12204/j.issn.1000-7229.2022.09.011

• Research and Application of Key Technologies for Distribution Network Planning and Operation Optimization under New Energy Power Systems ·Hosted by Professor WANG Shouxiang and Dr. ZHAO Qianyu· • Previous Articles     Next Articles

Net Load Forecasting Method Based on Feature-Weighted Stacking Ensemble Learning

BAO Haibo1(), WU Yangchen2(), ZHANG Guoying1(), LI Jiangwei1(), GUO Xiaoxuan3(), LI Jinghua2()   

  1. 1. Nanning Power Supply Bureau, Guangxi Power Grid Corporation, Nanning 530031, China
    2. Guangxi Key Laboratory of Power System Optimization and Energy Technology (Guangxi University), Nanning 530004, China
    3. Electric Power Research Institute, Guangxi Power Grid Corporation, Nanning 530023, China
  • Received:2022-01-23 Online:2022-09-01 Published:2022-08-31
  • Supported by:
    China Southern Power Grid Corporation of China Research Program(GXKJXM20190717)


At present, a large amount of new energy is connected to the user side, and the actual power load minus the power generated by the new energy (hereinafter referred to as “net load”) is usually used for prediction research. Due to the strong randomness of new energy power generation, net load has strong uncertainty and poor regularity, which makes it difficult to predict accurately. To this end, this paper proposes a net load prediction method based on the feature-weighted Stacking ensemble algorithm. Firstly, through the analysis of the prediction performance and difference of different prediction models, this paper chooses Long Short-Term Memory Network, Elman Neural Network, Random Forest Tree and Least Squares Support Vector Machine as stacking ensemble learners. Secondly, because the traditional Stacking ensemble prediction model ignores the differences between learners, the model’s prediction ability is insufficient. Therefore, this paper weights the features of the learners according to the prediction accuracy to correct the prediction error introduced by different learners. Finally, the measured data in the German TENNET area is analyzed as an example. The simulation results show that, compared with the single forecasting model and the traditional Stacking integrated forecasting method, the payload prediction method based on feature-weighted stacking ensemble learning has higher forecasting accuracy in sunny, cloudy, rainy, snowy and other weather conditions.

Key words: renewable energy, net load forecasting, stacking ensemble learning, feature weighting

CLC Number: