论文信息 - Research on PCA-LSTM-based Short-term Load Forecasting Method - 字舞流文

Research on PCA-LSTM-based Short-term Load Forecasting Method

Fang Qiansheng | Zhong Yongxiang | Xie Chenlei | Zhang Hongyan | Li Shanshou | Z. Hongyan | Fang Qiansheng | Zhong Yongxiang | Xie Chenlei | Li Shanshou

[1] Bo Zhao,et al. Stochastic Optimal Operation of Microgrid Based on Chaotic Binary Particle Swarm Optimization , 2016, IEEE Transactions on Smart Grid.

[2] Ming-Wei Chang,et al. Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 , 2004, IEEE Transactions on Power Systems.

[3] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.

[4] Daniel L. Marino,et al. Deep neural networks for energy load forecasting , 2017, 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE).

[5] Yuancheng Li,et al. An approach to forecast short-term load of support vector machines based on rough sets , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[6] Hongseok Kim,et al. Deep Neural Network Based Demand Side Short Term Load Forecasting , 2016 .

[7] Shyh-Jier Huang,et al. Short-term load forecasting via ARMA model identification including non-Gaussian process considerations , 2003 .