Grey New Information GOM(1,1) Model and its Application Based on Opposite-Direction Accumulated Generating and Background Value Optimization

Liao, De Gang; Luo, You Xin

doi:10.4028/www.scientific.net/amr.321.33

Cited by 4 publications

(1 citation statement)

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“…The other is to change the forward accumulation to the reverse accumulation to improve the model structure, such as the first-order reverse accumulative model (Che et al 2013;Xiao et al 2014). Reverse accumulation used more information about the new data than the old data, which can effectively improve the performance of the model (Liao & Luo 2011). Combining the advantages of these two aspects, this study used the fractional order reverse accumulative grey model to predict regional water demand, which had good research prospects and has attracted the attention of many scholars (Xiong et al 2019).…”

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confidence: 99%

Forecast of regional water demand based on NSGAII-FORAGM

Liu

2021

Water Supply

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Regional water demand is an important basic data for regional engineering planning, design and management. Making full use of multi-source data and prior knowledge to quickly and economically obtain high-precision regional water demand is of great significance to the optimal allocation of regional water resources. In order to accurately predict the regional water demand, this study took Yulin City as a research area to predict the water demand of the city from 2017 to 2019. Aiming at the oscillating characteristics of the regional water demand sequence and the over-fitting problem of traditional prediction models, this study proposed the non-dominated sorting genetic algorithm II-fractional order reverse accumulative grey model (NSGAII-FORAGM). The regional water demand oscillation sequence was transformed into a monotonically decreasing non-negative sequence. Based on the transformation sequence, an optimization model was constructed according to the two objective functions of ‘maximum (or minimum) order’ and ‘best fit to historical data’, and the NSGAII method were adopted to solve the model. The three model structures of ‘fractional order’, ‘reverse accumulation’ and ‘obtaining order through multi-objective optimization model ‘ were tested based on the water use sequence of the three sectors (industry, tertiary industry and domestic) in Yulin City, and the performance of the method is compared with NSGAII-IORAGM, NSGAII-FOFAGM and SOGA-FORAGM. The results showed that the average relative error of the model established in this study for the simulation of industry, tertiary industry (The tertiary industry is a technical name for the service sector of the economy, which encompasses a wide range of businesses), and domestic was 15.54%, 11.20%, 9.98% respectively. The average relative error of the model established in this study for the prediction of industry, tertiary industry and domestic was 9.46%, 7.9%, 1.8% respectively. For the simulation of water demand sequences in three sections, the simulation average relative errors of the other three models were not absolutely dominant except for the SOGA-FORAGM model. The average relative predicted error by the model in this study was the smallest (The relative errors of the three sequence predictions for industry, tertiary industry and domestic were lower than the relative errors of the optimal results of the comparison model, which were 0.97%, 0.72% and 4.5%, respectively), indicating that the model had certain applicability for the water demand prediction of various sectors (industry, tertiary industry and domestic) in the region compared with other models, and can improve the accuracy of the prediction results.

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