Photovoltaic Power Prediction Considering VMD-CNN-LSTM and Migration Learning Frameworks for Poor Data Areas

Author:

Wang Xinhong1,Gao Xuefeng1,Li Bingling2,Shi Yu1,Lu Xiaoming1,Yao Yiwen1,Wang Dingheng1,Xu Xin1,Li Hao1

Affiliation:

1. State Grid Jilin Electric Power Company Limited Economics and Technology Institute,Changchun,China,130021

2. Northeast Electric Power University,Jilin,China,132012

Publisher

IEEE

Reference6 articles.

1. Day-ahead and intraday multi-timescale optimal scheduling of integrated energy distribution networks taking into account carbon emissions[J];Mingjie;Power System Protection and Control,2023

2. Photovoltaic power prediction based on combined XGBoost-LSTM model[J];Tan;Acta energiae solaris sinica,2022

3. Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model

4. Alerting to Rare Large-Scale Ramp Events in Wind Power Generation

5. A spatial load forecasting method using generative adversarial networks in data-poor scenarios[J];Bai,2020

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