Research on power load forecasting of PCA-CNN-LSTM based on sliding window

Author:

Han Shuwei1,Ru Huitong1,Wang Guangling1,Fu Xuezhi1,Zhou Guoxu1,Yang Chengqiao1

Affiliation:

1. China Three Gorges Renewables (Qingyun) Co., Ltd,Dezhou,Shandong,China

Publisher

IEEE

Reference6 articles.

1. Short-term load forecasting method based on multi-neural network fusion;Pang;Electric Power Automation Equipment,2020

2. Multivariate load forecasting for integrated energy systems based on LSTM and multi-task learning;Sun;Automation of Electric Power Systems,2021

3. Power load forecast based on long short-term memory-singular spectrum analysis;Mathew;Energy Systems,2022

4. Short-term power load forecasting method based on improved hierarchical transfer learning and multi-scale CNN-BiLSTM-Attention;Ouyang;Power System Protection and Control,2023

5. Ultra-short-term Power Load Forecasting Based on Cluster Empirical Mode Decomposition of CNN-LSTM;Liu;Power System Technology,2021

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