摘要详情

ID / 提交时间

72 / 2024-08-15 22:05:51

标题

Short-term Load Forecasting Method Based on PSO-VMD-BiLSTM-BiGRU Model

关键字

Electric load forecasting; Correlation analysis; Multi-variable loads; VMD; LSTM; GRU

主题及专题

lAI Applications in Engineering

状态

终稿

作者

YinJiaojiao / Harbin Institute of Technology

YangJian / No. 703 Research Institute of CSSC

ZhangZhenyu / No. 703 Research Institute of CSSC

ZhengWenbin / Harbin Institute of Technology；School of Electronics and Information Engineering； Harbin 150080； P.R. China

摘要

With the advancement of smart grid technologies and the emergence of novel energy systems, loads characterized by diversity and flexibility have become a critical component of these systems. Research on their prediction models is essential for the operation, maintenance, and planning of novel energy systems. This paper presents a short-term joint forecasting method for multi-variable loads based on PSO-VMD-LSTM-GRU, taking into account the coupling characteristics of multi-variable loads and the correlations with other factors. First, various correlation analysis methods are employed to study the coupling characteristics of multi-variable loads and the correlations of influencing factors, and predictive features are selected accordingly. Second, variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is utilized to decompose the multi-variable loads, thereby improving load utilization. Finally, LSTM and GRU models are employed to forecast the low-frequency and high-frequency components, respectively, and the predicted results are combined to obtain the final forecast. The effectiveness of the proposed method is validated through experiments using data from the ASHRAE-Great Energy Predictor III competition project, comparing it with conventional load forecasting methods.