Load Trend Prediction Analysis based on LSTM and Sliding Time Window-Reference-Cited by-同舟云学术

Load Trend Prediction Analysis based on LSTM and Sliding Time Window

Published:2022-12-16 Issue: Volume: Page:
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Container-title:2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)
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Author:

Zhao Jinxiong¹,Zhang Xun¹,Wang Hai²,Mu Dejun³

Affiliation:

1. State Grid Gansu Electric Power Research Institute,Lanzhou,China,730070

2. Lanzhou Longneng Electric Power Science&Technology Ltd.

3. Research & Development Institute of Northwestern Polytechnical University in Shenzhen,Shenzhen,Guangdong,China,518057

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10019340/10019341/10019830.pdf?arnumber=10019830

Reference5 articles.

1. Research on Short-term Load Forecasting Variable Selection Based on Fusion of Data Driven Method and Forecast Error Driven Method;zheng;Proceedings of the CSEE,0

2. Medium and long term load forecasting method Considering Multi-time scale data;luo;Proceedings of the CSEE,0

3. Tsne Dimension Reduction Visualization Analysis and Moth Flame Optimized ELM Algorithm Applied in Power Load Forecasting;zhang;Proceedings of the CSEE,0

4. A survey of data-driven load forecasting for integrated energy systems;zhu;Proceedings of the CSEE,0

5. Short-term load forecasting method based on cuckoo search algorithm and support vector machine considering demand price elasticity;su;Journal of Jiangsu University Natural Science,2022

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