Bottom-up daily load profile forecasting of group households

被引：0
作者：
Zhu Y. [1 ]
Gao B. [1 ]
Chen N. [2 ]
Zhu Z. [1 ]
Liu X. [1 ]
Qin Y. [1 ,3 ]
机构：
[1] School of Electrical Engineering, Southeast University, Nanjing
[2] State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems, China Electric Power Research Institute, Nanjing
[3] Electric Power Research Institute of State Grid Xinjiang Electric Power Co., Ltd., Urumqi
来源：
Gao, Bingtuan (gaobingtuan@seu.edu.cn) | 1600年 / Southeast University卷 / 50期
关键词：
Bottom-up; Daily load forecasting; Modularization; Monte Carlo simulation; Power consumption behavior;
D O I：
10.3969/j.issn.1001-0505.2020.01.007
中图分类号：
学科分类号：
摘要：
To improve the forecasting accuracy of the daily load of group households, a modular daily load forecasting method for group residents was presented based on the bottom-up modeling idea. Considering the influences of external factors and user's own behavior on the load, a similar day extraction module, a cluster analysis module and a user electricity behavior analysis module were established to realize daily load forecasting for a single-family household. Consequently, by using Monte Carlo sampling method to simulate three random variables: household appliance combination, household appliance power and user power consumption time point, the user load prediction module was established to realize the daily load forecasting for group households. Simulation results by a case study indicate that the average error and the maximum error of daily load forecasting for group households are 1.3% and 5.6%, respectively. Compared with the forecasting method based on grey model with the average error 2.7% and the maximum error 2.5%, and the forecasting method based on neural network model with the average error 2.3% and the maximum error 6.9%, the proposed method can improve forecasting accuracy of the daily load of group households. © 2020, Editorial Department of Journal of Southeast University. All right reserved.
引用
收藏
页码：46 / 55
页数：9
相关论文
共 20 条
  • [1] Chen S.Y., Song S.F., Li L.X., Et al., Survey on smart grid technology, Power System Technology, 33, 8, pp. 1-7, (2009)
  • [2] Dong Z.Y., Zhao J.H., Wen F.S., Et al., From smart grid to energy Internet: Basic concept and research framework, Automation of Electric Power Systems, 38, 15, pp. 1-11, (2014)
  • [3] Chen H., Wan Q.L., Wang Y.R., Short term load forecasting method based on auto-regressive conditional density model, Journal of Southeast University (Natural Science Edition), 44, 3, pp. 561-566, (2014)
  • [4] Liu X.F., Gao B.T., Li Y., Review on application of game theory in power demand side, Power System Technology, 42, 8, pp. 2704-2711, (2018)
  • [5] Yang X.Y., Zhou M., Li G.Y., Survey on demand response mechanism and modeling in smart grid, Power System Technology, 40, 1, pp. 220-226, (2016)
  • [6] Huang H.X., Deng L., Wen F., Et al., Customer response behavior based on real-time pricing, Electric Power Construction, 37, 2, pp. 63-68, (2016)
  • [7] Grandjean A., Adnot J., Binet G., A review and an analysis of the residential electric load curve models, Renewable and Sustainable Energy Reviews, 16, 9, pp. 6539-6565, (2012)
  • [8] Lin S.F., Huang N.N., Zhao L.J., Et al., Domestic daily load curve modeling based on user behavior, Electric Power Construction, 37, 10, pp. 114-121, (2016)
  • [9] Chuan L., Ukil A., Modeling and validation of electrical load profiling in residential buildings in Singapore, IEEE Transactions on Power Systems, 30, 5, pp. 2800-2809, (2015)
  • [10] Pflugradt N., Muntwyler U., Synthesizing residential load profiles using behavior simulation, Energy Procedia, 122, pp. 655-660, (2017)
12