Short-term Load Forecast Using Improved Long-short Term Memory Network
Author:
Affiliation:
1. Tsinghua University,Beijing,China
2. China Electric Power Research Institute Co. Ltd.,Beijing,China
3. State Grid Zhejiang Electric Power Research Institute,Hangzhou,China
Funder
Science and Technology Project of State Grid
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9845798/9845816/09845931.pdf?arnumber=9845931
Reference20 articles.
1. Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems
2. Short-Term Load Forecasting Based on Deep Neural Networks Using LSTM Layer
3. Comparison of Short-Term Load Forecasting Techniques
4. Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †
5. Deep Learning-Based Forecasting Approach in Smart Grids With Microclustering and Bidirectional LSTM Network
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1. Fusion of Hierarchical Optimization Models for Accurate Power Load Prediction;Sustainability;2024-08-12
2. A Comparative Analysis of Deep Learning Models for Short-Term Load Forecasting;2023 IEEE 19th International Conference on Automation Science and Engineering (CASE);2023-08-26
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