Open Access
| Issue |
E3S Web Conf.
Volume 53, 2018
2018 3rd International Conference on Advances in Energy and Environment Research (ICAEER 2018)
|
|
|---|---|---|
| Article Number | 02006 | |
| Number of page(s) | 4 | |
| Section | Energy Equipment and Application | |
| DOI | https://doi.org/10.1051/e3sconf/20185302006 | |
| Published online | 14 September 2018 | |
- Y. Ying, W. Zhiqiang, D. Bo. Research and Analysis of Residential Electricity Consumption Behavior Based on Big Data [J]. ELECTRIC POWER ICT, 2015, 13(11): 7-11. [Google Scholar]
- Y. X. Li, S. L. Fang, L. Q. Yu. et al. Power system short-term load forecasting based on fuzzy clustering analysis and BP neural network[J] Power System Technology, 2005, 29(1): 20-23. [Google Scholar]
- Tranchita C., Torres. Soft computing techniques for short term load forecasting[C]. 2004 IEEE PES Power Systems Conference & Exposition, New York, USA, 2004, :1497-502. [Google Scholar]
- Osowski S., Siwek. The selforganizing neural network approach to load forecasting in the power system[C]. International Joint Conference on Neural Networks. Washington DC, USA, 1999, 5:3401-3404. [CrossRef] [Google Scholar]
- Beccali M., Cellura M., Brano V. L.. Forecasting daily urban electric load profiles using artificial neural networks[J]. Energy Conversion and Management, 2004, 45(18): 2879-2900. [Google Scholar]
- Fatima R, Jorge D, Vera F.. A comparative analysis of clustering algorithms applied to load profiling[C]. The Third International Conference on Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany, 2003:73-85. [Google Scholar]
- Duarte F. J., Rodrigues F., Figueiredo V.. Data mining techniques applied to electric energy consumers characterization[C]. Proceedings of the Seventh International Conference on Artificial Intelligence and Soft Computing, Banff, Canada, 2003 :105-110. [Google Scholar]
- Y. Jiang, Z. Q. Wang, B. Dai. Research and Analysis of Household Electricity Consumption Habits Based on Big Data [J]. ELECTRIC POWER ICT, 2015, 13(11): 7-11. [Google Scholar]
- Y. Sun, T. T. Zang, F. Jiang. Analysis of Enterprise Power User Load Characteristics under the Background of Big Data [J]. Statistics & Decision, 2018, 34(08): 186-188. [Google Scholar]
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