Open Access
| Issue |
E3S Web Conf.
Volume 182, 2020
2020 10th International Conference on Power, Energy and Electrical Engineering (CPEEE 2020)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 5 | |
| Section | Advanced Power Generation Technology and Application | |
| DOI | https://doi.org/10.1051/e3sconf/202018201004 | |
| Published online | 31 July 2020 | |
- Q.W. Ran, Y.Z. Shan, Q. Wang, et al., Wavelet neural network-PARIMA method for short-term load forecasting of power system, Chinese journal of electrical engineering 23(3), 38-42 (2003) [Google Scholar]
- Y. Du, Z. Guo, L.Z. lu, et al., Overview of short-term power system load forecasting methods, Technology and market 5, 339-340 (2015) [Google Scholar]
- D. Alberg, M. Last, Short-Term Load Forecasting in Smart Meters with Sliding Window-Based ARIMA Algorithms, (2017) [Google Scholar]
- L. Ghelardoni, A. Ghio, D. Anguita, Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression, IEEE Transactions on Smart Grid 4(1), 549-556 (2013) [Google Scholar]
- J.B. Fiot, F. Dinuzzo, Electricity Demand Forecasting by Multi-Task Learning, IEEE Transactions on Smart Grid, 1-1 (2016) [Google Scholar]
- J.G. Jetcheva, M. Majidpour, W.P. Chen, Neural network model ensembles for building-level electricity load forecasts, Energy and Buildings 84, 214-223 (2014) [Google Scholar]
- J. Ku, R. Goomer, A.K. Singh, Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters, Procedia Computer Science 125, 676-682 (2018) [Google Scholar]
- Y.H. Zhang, C.M. Qiu, X. He, et al., A Short-Term Load Forecasting Based on LSTM Neural Network[J], Electric Power Information & Communication Technology, (2017) [Google Scholar]
- F. Rosenblatt, Perceptron Simulation Experiments, Proceedings of the Ire 48(3), 301-309 (1960) [CrossRef] [Google Scholar]
- J.L. Elman. Finding structure in time, Cognitive Science 14(2), 179-211 (1990) [Google Scholar]
- S. Hochreiter, J. Schmidhuber, Flat Minima 9(1), 1 (1997). [Google Scholar]
- G. Gelly, J.L. Gauvain, Optimization of RNN- Based Speech Activity Detection, IEEE/ACM Transactions on Audio Speech & Language Processing 99, 1 (2017). [Google Scholar]
- K. Kang, H.B. Sun, C.K. Zhang, et al., Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network, Evolutionary Intelligence, (2019) [Google Scholar]
- Z.Y. Tian, M.L. Zhang, R. Zhao. Identification and correction of abnormal load data in short-term power load forecasting. Jilin electric power 6, 25-27 (2004) [Google Scholar]
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