Short-term consumer load probability density forecasting based on EMD-QRF

被引：0
|
作者：
Yang B. [1 ]
Yang S. [2 ]
Cao X. [2 ]
Chen Y. [2 ]
Liang Z. [3 ]
Sun G. [3 ]
机构：
[1] State Grid Jiangsu Electric Power Co., Ltd., Nanjing
[2] State Grid Jiangsu Electric Power Company Research Institute, Nanjing
[3] College of Energy and Electrical Engineering, Hohai University, Nanjing
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2019年 / 47卷 / 16期
基金：
中国国家自然科学基金;
关键词：
Consumer load; Empirical mode decomposition; Kernel density estimation; Probability density forecasting; Quantile regression forest;
D O I：
10.19783/j.cnki.pspc.181207
中图分类号：
学科分类号：
摘要：
Considering the small base of consumer load time series, strong volatility and randomness, along with the difficulty of obtaining high forecasting accuracy, a hybrid model based on Empirical Mode Decomposition (EMD) and Quantile Regression Forest (QRF) is proposed for consumer load probability density forecasting, which is aimed at enhancing prediction precision. Firstly, the signal processing algorithm of EMD is applied to decompose the original consumer load time series, where the sample entropy of each decomposed mode function is calculated. Based on the values of sample entropy, the mode functions can be reconstructed. Then, each reconstructed component is modeled separately using QRF for consumer load forecasting, where the conditional distribution of predicted values can be obtained by superimposing prediction results of different components. Finally, the Kernel Density Estimation (KDE) is used to output the consumer load probability density forecasting results at any time. Compared with deterministic point prediction methods, the proposed probability density forecasting model has advantages of describing the possible fluctuation range and uncertainty of the consumer load in the future, where the case study has also verified its validity. © 2019, Power System Protection and Control Press. All right reserved.
引用
收藏
页码：1 / 7
页数：6
相关论文
共 21 条
  • [1] China Energy Network, Deviation electricity assessment helps to form an oligopoly in the electricity sales market
  • [2] Li R., Zhu F., Zhu Y., Et al., Short-term power load forecasting using recurrent neural network with restricted Boltzmann machine, Power System Protection and Control, 46, 17, pp. 83-88, (2018)
  • [3] Quilumba F.L., Lee W.J., Huang H., Et al., Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities, IEEE Transactions on Smart Grid, 6, 2, pp. 911-918, (2015)
  • [4] Yang H., Deng Y., Liu Z., Et al., Study on electric load forecasting with historical bad data, Power System Protection and Control, 45, 15, pp. 62-68, (2017)
  • [5] Liu Y., Zhang H., Zhang A., Improved load forecasting method based on load characteristics under demand-side response, Power System Protection and Control, 46, 13, pp. 126-133, (2018)
  • [6] Gao X., Yang S., Pan S., A forecasting model for output power of grid-connected photovoltaic generation system based on EMD and ABC-SVM, Power System Protection and Control, 43, 21, pp. 86-92, (2015)
  • [7] Jain R.K., Smith K.M., Culligan P.J., Et al., Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy, Applied Energy, 123, 3, pp. 168-178, (2014)
  • [8] Ahmad A.S., Hassan M.Y., Abdullah M.P., Et al., A review on applications of ANN and SVM for building electrical energy consumption forecasting, Renewable & Sustainable Energy Reviews, 33, 2, pp. 102-109, (2014)
  • [9] Gan W., Short-term load forecasting of small load heavy wave area model research, (2007)
  • [10] Huang L., Yang Y., Zhao H., Et al., Time series modeling and filtering method of electric power load stochastic noise, Protection and Control of Modern Power Systems, 2, 2, pp. 269-275, (2017)
123