A research on a comprehensive adaptive grey prediction model CAGM(1,N)

Guo, Huan; Xiao, Xinping; Forrest, Jeffrey Yi‐Lin

doi:10.1016/j.amc.2013.08.094

Cited by 35 publications

(21 citation statements)

References 17 publications

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“…The grey system theory is established by Deng in 1982 [3], mainly based on small sample with partial information known and partial information unknown [4]. It mainly focuses on analysis of uncertainty and poor information system [5].…”

Section: A Gm(11) Modelmentioning

confidence: 99%

Prediction of Low-Rent Housing's Supply Quantity Based on Grey Systems Theory

Li¹

2013

2013 Sixth International Conference on Business Intelligence and Financial Engineering

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In this paper, we use grey GM (1, 1) model to predict low rental housings' supply quantity in Beijing from 2013 to 2015.Firstly, we give a brief introduction of renewal grey GM (1, 1) model and its methodology. Secondly, we use per capital floor space of urban residential buildings and nonagricultural population as basic data to predicts Beijing's total housing supply from 2014 to 2016. Finally, we use grey GM (1, 1) model predict the number and the area of low rent housings' supply target in Beijing

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Section: A Gm(11) Modelmentioning

confidence: 99%

Prediction of Low-Rent Housing's Supply Quantity Based on Grey Systems Theory

Li¹

2013

2013 Sixth International Conference on Business Intelligence and Financial Engineering

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“…However, the irrigation object is a large inertia, nonlinear and pure delay system, it cannot establish a precise and unified mathematical model, so, the traditional control method has been a serious challenge [4][5][6]. Fuzzy control does not need to establish the mathematical model of the controlled object, robust and effective to improve its nonlinear and time-varying problems [7][8][9], but the lack of large delay phenomenon.…”

Section: Introductionmentioning

confidence: 99%

MBD of grey prediction fuzzy-PID irrigation control technology

Zhang¹,

Wei²,

Lin³

et al. 2018

Desalination and Water Treatment

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a b s t r a c tFor the water and fertilizer ratio adjustment, irrigation control precision and problems and development cycle in the agricultural water-fertilizer irrigation, the control model of the combination of proportion-integral-derivative (PID) control, fuzzy control and grey prediction control were designed, and the controller design was carried out based on the model design method. It can achieve the engineering code to the embedded hardware requirements of the whole process of automation, efficiency of water and fertilizer irrigation controller, rapid development and application. PID control was based on fuzzy rules and query fuzzy matrix table to obtain parameter self-turning, then it could adjust PID parameters in real time, furthermore, joined multi-factors grey forecasting multi-variable grey model (1, n), so as to effectively predict the water requirement of the crop and achieve the precise irrigation of the system. The running results the system reduces the overshoot by 14.68% compared with the conventional PID control, and the adjustment time of the system is reduced by 86.62%. Compared with the fuzzy PID control, the overshoot is slightly increased by 1.9%, but the adjustment time is reduced by 177.71%. Meanwhile, in the water and fertilizer concentration control system, the controller is developed quickly, the code quality is high, the system response speed is good, the real time is good and stable, and the effect of water and fertilizer is greatly improved.

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“…However, these parameters can also be calculated by other optimization methods, which may enhance the prediction performance of GM(1,N). Guo et al [24] used a comprehensive adaptive grey model, CAGM(1,N), to forecast traffic flow and showed that the prediction accuracy of GM(1,N) is significantly improved by optimizing background values. In addition, several optimization techniques have been suggested to modify GM(1,N) models.…”

Section: Grey Prediction Modelsmentioning

confidence: 99%

An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

Xie

Zhang

et al. 2018

Energies

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Abstract:In this paper, an improved grey model and scenario analysis, GA-GM(1,N) is proposed to forecast the carbon intensity in the Pearl River Delta (PRD) region, one of the most developed regions in China. Moreover, to show the advantage and feasibility of the proposed model, the forecasting results of the GA-GM(1,N) model are compared with that of a single-variable grey model (GM (1,1)) and a multivariable form (GM(1,N)). Data from one sample period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) are used to develop the models, and data from another sample period (2013)(2014)(2015) are used to test them. The mean absolute percentage error (MAPE) is applied to measure the accuracy of prediction. The results show that, of the three models, GA-GM(1,N) produces the best carbon intensity forecasts, with MAPEs of 0.4-1.4% and 0.04-0.4% in the development and testing periods respectively. This indicates that the optimization of the genetic algorithm is effective. The realization of carbon reduction targets in different cities is also explored by combining grey models with scenario analysis. Only Guangzhou could achieve its reduction target under all scenarios, and it can serve as a reference for other cities. Policy recommendations are provided based on these results.

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A research on a comprehensive adaptive grey prediction model CAGM(1,N)

Cited by 35 publications

References 17 publications

Prediction of Low-Rent Housing's Supply Quantity Based on Grey Systems Theory

Prediction of Low-Rent Housing's Supply Quantity Based on Grey Systems Theory

MBD of grey prediction fuzzy-PID irrigation control technology

An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

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