1 I. Sutskever, "Training recurrent neural networks" University of Toronto 2013
2 J. Zheng, "Smart meters in smart grid: an overview" 57-64, 2013
3 Q. Xiaoyun, "Short-term prediction of wind power based on deep long short-term memory" 1148-1152, 2016
4 H. Salehinejad, "Recent advances in recurrent neural networks"
5 Y. He, "Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism" 8 (8): 2505-2516, 2017
6 Y. Bottou, "Large-Scale Kernel Machines" MIT Press 2007
7 A. Pulver, "LSTM with working memory" 846-851, 2017
8 I. Colak, "Introduction to smart grid" 30-34, 2016
9 S. Yao, "Deep learning for the internet of things" 51 (51): 32-41, 2018
10 I. Goodfellow, "Deep Learning" MIT Press 2016
1 I. Sutskever, "Training recurrent neural networks" University of Toronto 2013
2 J. Zheng, "Smart meters in smart grid: an overview" 57-64, 2013
3 Q. Xiaoyun, "Short-term prediction of wind power based on deep long short-term memory" 1148-1152, 2016
4 H. Salehinejad, "Recent advances in recurrent neural networks"
5 Y. He, "Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism" 8 (8): 2505-2516, 2017
6 Y. Bottou, "Large-Scale Kernel Machines" MIT Press 2007
7 A. Pulver, "LSTM with working memory" 846-851, 2017
8 I. Colak, "Introduction to smart grid" 30-34, 2016
9 S. Yao, "Deep learning for the internet of things" 51 (51): 32-41, 2018
10 I. Goodfellow, "Deep Learning" MIT Press 2016
11 G. N. Srinivasa Prasanna, "Data communication over the smart grid" 273-279, 2009
12 R. Morello, "A smart power meter to monitor energy flow in smart grid: The role of advanced sensing and IoT in the electric grid of the future" 17 (17): 7828-7837, 2017
13 H. Yang, "A practical pricing approach to smart gird demand response based load classification" 9 (9): 179-190, 2018
14 "A Joint Project of the EEI and AEIC Meter Committees, Smart meters and smart meter systems: A metering industry perspective"