Short-Term Power Load Forecasting Based on VMD-SSA-LSTM
Author:
Affiliation:
1. State Grid Yichang Power Supply Company,Yichang,P.R.China,443002
2. China Three Gorges University,Yichang,P.R.China,443002
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9991245/9991269/09991639.pdf?arnumber=9991639
Reference20 articles.
1. Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior
2. Deep Flexible Transmitter Networks for Non-Intrusive Load Monitoring of Power Distribution Networks
3. Electrical Energy Prediction in Residential Buildings for Short-Term Horizons Using Hybrid Deep Learning Strategy
4. Short-Term Load Forecasting Based on PSO-KFCM Daily Load Curve Clustering and CNN-LSTM Model
5. A Novel Selection Approach for Genetic Algorithms for Global Optimization of Multimodal Continuous Functions
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1. Ultra-Short-Term Offshore Wind Power Prediction Based on PCA-SSA-VMD and BiLSTM;Sensors;2024-01-11
2. A Novel LSTM-XGBoost Model Optimized by SSA for Predicting Short-Term Photovoltaic Power;2023 3rd International Conference on Energy, Power and Electrical Engineering (EPEE);2023-09-15
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