Modification method to deal with the accumulation effects for summer daily electric load forecasting

Li, Yang; Bao, Yu-Qing; Yang, Bin; Chen, Chu; Ruan, Wenjun

doi:10.1016/j.ijepes.2015.06.027

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…Thus GM (1,1) model is selected as the predictive model for Guizhou total electricity consumption(Yang Li et al 2015).…”

Section: Selection Of Forecasting Modelmentioning

confidence: 99%

“…And the initial value of the gray prediction GM (1,1) model is improved based on the principle of least squares method. In literature [5], a load forecasting method based on chaotic time series is used to forecast monthly electricity consumption. Literature [6] proposes a power system load forecasting method based on the combination of time series and neural networks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research of combination of electricity GM (1,1) and seasonal time series forecasting model

Wang¹,

Zheng²,

He³

et al. 2015

Proceedings of the 4th International Conference on Information Technology and Management Innovation

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Load forecasting is one of the important content of power system. Gray GM (1,1) model with seasonal time series was proposed by analyzing the development of the electricity load forecasting. This paper made an empirical analysis of the model with data specifically related to electricity in Guizhou region in recent years. The results showed that the proposed method has a higher accuracy than a single prediction model, and proved correctness and effectiveness of the combination forecasting model.

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“…Thus GM (1,1) model is selected as the predictive model for Guizhou total electricity consumption(Yang Li et al 2015).…”

Section: Selection Of Forecasting Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%