Application of fractional order-based grey power model in water consumption prediction

2019 ◽  
Vol 78 (8) ◽  
Author(s):  
Yanbin Yuan ◽  
Hao Zhao ◽  
Xiaohui Yuan ◽  
Liya Chen ◽  
Xiaohui Lei
Keyword(s):  
Fractional Order ◽  
Water Consumption ◽  
Power Model ◽  
Consumption Prediction

A novel fractional discrete grey model with an adaptive structure and its application in electricity consumption prediction

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yitong Liu ◽  
Yang Yang ◽  
Dingyu Xue ◽  
Feng Pan
Keyword(s):  
Fractional Order ◽  
Electricity Consumption ◽  
Grey Model ◽  
The Novel ◽  
Raw Data ◽  
Content Type ◽  
Power Load ◽  
Grey Models ◽  
Novel Model ◽  
Consumption Prediction

PurposeElectricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.Design/methodology/approachThe novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.FindingsTwo cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.Originality/valueA fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.

scholarly journals Analysis of dynamic evolution and driving factors behind water consumption in China

2017 ◽  
Vol 18 (3) ◽  
pp. 1093-1102 ◽  
Author(s):  
Subing Lü ◽  
Fuqiang Wang ◽  
Yumin Yu ◽  
Huayu Zhong ◽  
Shiguo Xu
Keyword(s):  
Water Consumption ◽  
Food Production ◽  
Principal Component ◽  
Driving Factors ◽  
National Scale ◽  
Consumption System ◽  
Shape Curve ◽  
Two Stages ◽  
Urban Consumption ◽  
Consumption Prediction

Abstract The water consumption system is a typical dissipative structure system, and its evolution can be described with information entropy. Meanwhile understanding the principal driving factors in the evolution of water consumption is essential for water consumption prediction and management. Firstly, the information entropies of water consumption in China were calculated from 1997 to 2010. Secondly, the principal driving factors were extracted using principal component analysis. Finally, based on the principal driving factors, the water consumption system was predicted. The results showed that the entropies can be divided into two stages: an entropy increasing period (1997–2002) and an entropy convergence period (2003–2010). On a national scale, the entropies in the majority of provinces are focused between 0.6 and 1.1. The principal driving factors were population, gross domestic product, food production, command irrigation area, and urban consumption levels. Chinese water consumption structure will develop an inverted ‘U’-shape curve and water consumption levels are expected to plateau during 1997 to 2020. The system is gradually becoming more orderly through coordination and self-organization.

scholarly journals ARIMA-M: A New Model for Daily Water Consumption Prediction Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 760 ◽  
Author(s):  
Hongyan Du ◽  
Zhihua Zhao ◽  
Huifeng Xue
Keyword(s):  
Markov Chain ◽  
Water Consumption ◽  
Prediction Error ◽  
Moving Average ◽  
Arima Model ◽  
Regional Environment ◽  
Consumption Data ◽  
Daily Water ◽  
Consumption Prediction ◽  
Daily Water Consumption

Water resource is considered as a significant factor in the development of regional environment and society. Water consumption prediction can provide an important decision basis for the regional water supply scheduling optimizations. According to the periodicity and randomness nature of the daily water consumption data, a Markov modified autoregressive moving average (ARIMA) model was proposed in this study. The proposed model, combined with the Markov chain, can correct the prediction error, reduce the continuous superposition of prediction error, and improve the prediction accuracy of future daily water consumption data. The daily water consumption data of different monitoring points were used to verify the effectiveness of the model, and the future water consumption was predicted in the study area. The results show that the proposed algorithm can effectively reduce the prediction error compared to the ARIMA.

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