[1]赵俊华, 董朝阳, 文福拴, 等.面向能源系统的数据科学:理论、技术与展望 [J].电力系统自动化, 2017, 41(4): 1-11.
ZHAO Junhua, DONG Zhaoyang, WEN Fushuan, et al.Data science for energy systems: Theory, techniques and prospect [J]. Automation of Electric Power Systems, 2017, 41(4): 1-11.
[2]KAVOUSI-FARD A,SAMET H,MARZBANI F. A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting [J]. Expert Systems with Applications, 2014, 41(13): 6047-6056.
[3]SRIVASTAVA A K, PANDEY A S, SINGH D. Short-term load forecasting methods: A review [C] // International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems. IEEE, 2016: 130-138.
[4]MARINO D L, AMARASINGHE K, MANIC M. Building energy load forecasting using deep neural networks [C] // The 42nd Annual Conference of the IEEE Industrial Electronics Society (IECON 2016). IEEE, 2016: 7046-7051.
[5]BUSSETI E, OSBAND I, WONG S. Deep learning for time series modeling [R]. Technical report, Stanford University, 2012.
[6]QIU X, ZHANG L, REN Y, et al. Ensemble deep learning for regression and time series forecasting [C] // International Conference on Computational Intelligence in Ensemble Learning. IEEE, 2015: 1-6.
[7]DIN G M U, MARNERIDES A K. Short term power load forecasting using deep neural networks [C] // International Conference on Computing, Networking and Communications. IEEE, 2017: 594-598.
[8]孔祥玉, 郑锋, 鄂志君, 等. 基于深度信念网络的短期负荷预测方法 [J]. 电力系统自动化, 2018, 42(5): 133-139.
KONG Xiangyu, ZHENG Feng, E Zhijun, et al. Short-term load forecasting based on deep belief network [J]. Automation of Electric Power Systems, 2018, 42(5): 133-139.
[9]陈卓, 孙龙祥. 基于深度学习 LSTM 网络的短期电力负荷预测方法 [J]. 电子技术, 2018(1): 39-41.
CHEN Zhuo,SUN Longxiang. Short-term electrical load forecasting based on deep learning LSTM networks [J]. Electronic Technology, 2018(1): 39-41.
[10]方志强, 王晓辉, 夏通. 基于长短期记忆网络的售电量预测模型研究 [J]. 电力工程技术, 2018, 37(3): 78-83.
FANG Zhiqiang, WANG Xiaohui, XIA Tong. Electricity sales forecasting based on long-short term memory networks [J]. Electric Power Engineering Technology, 2018, 37(3): 78-83.
[11]董浩, 程鹏, 李玲玲. 深度学习算法在电力系统短期负荷预测中的应用 [J]. 电气时代, 2017 (2): 82-84.
DONG Hao, CHENG Peng, LI Lingling. Application of deep learning in power system short-term load forecasting [J]. Electric Age, 2017 (2): 82-84.
[12]HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets [J]. Neural Computation, 2006, 18(7): 1527-1554.
[13]付文博, 孙涛, 梁藉, 等. 深度学习原理及应用综述 [J]. 计算机科学, 2018, 45(6A): 11-15.
FU Wenbo, SUN Tao, LIANG Ji, et al. Review of principle and application of deep learning [J]. Computer Science, 2018, 45(6A): 11-15.
[14]HUANG F J, LECUN Y. Large-scale learning with SVM and convolutional for generic object categorization [C] // 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2006: 284-291.
[15]SALAKHUTDINOV R, LAROCHELLE H. Efficient learning of deep Boltzmann machines [C] // 13th International Conference on Artificial Intelligence and Statistics (AISTATS). 2010: 693-700.
[16]HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313(5786): 504-507.
[17]GERS F A, SCHMIDHUBER E. LSTM recurrent networks learn simple context-free and context-sensitive languages [J]. IEEE Transactions on Neural Networks, 2001, 12(6): 1333-1340.
[18]SCHUSTER M, PALIWAL K K. Bidirectional recurrent neural networks [J]. IEEE Transactions on Signal Processing, 1997, 45(11): 2673-2681.
[19]GRAVES A, MOHAMED A R, HINTON G. Speech recognition with deep recurrent neural networks [C] // IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2013: 6645-6649.
[20]SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: A simple way to prevent neural networks fromoverfitting [J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
[21]DUDEK G. Pattern-based local linear regression models for short-term load forecasting [J]. Electric Power Systems Research, 2016, 130: 139-147. |