Linear Regression Electricity Prediction Method Based on Clustering of Electric Characteristics

Author:

Xiangyu Zhou1,Qingqing Miao1,Tao Lin1,Lei Zhang1,Jianquan Zhou1

Affiliation:

1. State Grid Jining Power Supply Company, Jining City, China

Publisher

ACM Press

Reference11 articles.

1. Zhao Y., Li L., and Liu J. Y., et al. 2010. Combinational recognition model for demand side load profile in shanghai power grid. Power System Technology. 34, 1(Jan. 2010), 145--151.

2. Zhao T., Wang L. T. and Zhang Y., et al. 2016. Relation factor identification of electricity consumption behavior of users and electricity demand forecasting based on mutual information and random forests. Proceedings of the CSEE. 36,3(Feb. 2016), 604--614

3. Zhao T., Zhang Y., and Zhang D. X. 2014. Application technology of big data in smart distribution grid and its prospect analysis. Power System Technology. 38, 12 (Dec. 2014), 3305--3312

4. Rallapalli S. R. and Ghosh S.. 2012. Forecasting monthly peak demand of electricity in India-A critique. Energy Policy. 45,2 (Jun. 2012), 516--520.

5. Ali O. P. and H. K. C.. 2013. ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient. Journal of Hydrology. 500, 11(Jun. 2013), 21--36

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