Application of Grey Forecasting Model to Load Forecasting of Power System

Yan, Yan; Liu, Chunfeng; Qu, Bin; Zhao, Quanming; Ji, Feifei

doi:10.1007/978-3-642-27452-7_27

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…The GM(1,1) forecasting model is the basic and most popular of the family of Grey prediction models. According to Yan et al [54], the model creates a first-order accumulation production for a series ( )…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey of Selected Grey Forecasting Models with Application to Medical Tourism Forecasting

Foucade¹,

Gabriel²,

Scott³

et al. 2019

TEL

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This paper examines the predictive capacity of two Grey Systems Forecasting models. The original Grey GM(1,1) Forecasting model, introduced by Deng [1] [2] together with an improved Grey GM(1,1) model proposed by Ji et al. [3] are used to forecast medical tourism demand for Bermuda. The paper also introduces a quasi-optimization method for the optimization of the alpha (weight) parameter. Five steps ahead out-of-sample forecasts are produced after estimating the models using four data points. The results indicate that the optimization of the alpha parameter substantially improves the predictive accuracy of the models; reducing the five steps ahead out-of-sample Mean Absolute Percentage Error from roughly 7% to roughly 3.80% across the two models. Largely, the forecasting approaches demonstrate significant potential for use as an alternative to the traditional forecasting methods in circumstances where substantial amounts of high-quality data are not available.

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Section: Literature Reviewmentioning

confidence: 99%

A Survey of Selected Grey Forecasting Models with Application to Medical Tourism Forecasting

Foucade¹,

Gabriel²,

Scott³

et al. 2019

TEL

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“…However, there was basic information and monitoring data which were calculated by month or week, monitoring errors, data cycle variation and other issues in the parameter system. Thus, this paper has adopted the gray prediction model to determine the data variation model and variation curve based on monitoring data [12]. The corresponding determined value of 365 days would be obtained from the variation curve respectively.…”

mentioning

confidence: 99%

Constructing the Model of Water Value Balance and Simulation Platform of Urban Water System in Harbin, China

Liu

You

2015

AMR

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This study proposed the theory of water value balance and built the dynamic model system, based on the water value balance index. To solve dynamic weight issues, the entropy weight model was used, while the gray forecasting model was used for supplement missing data. Simulation platform integrates mathematical module, simulation module, property database and spatial database of urban water system in Harbin, which based on Matlab, C#, CloudSim, Oracle, ArcGIS and its secondary development technology. The results showed that urban water systems had a strong capacity to utilize water to capture value between May and mid-September, the value obtained was not sufficient to compensate the loss caused in the periods from January to April and from late September to December. The loss of water due to unknown causes was close to 7%, while the cost of water quality restoration accounted for 0.89% of GDP.

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