Fractional-order discrete grey models for China’s electricity consumption forecasting

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.

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A Novel Grey Power-markov Model for the Prediction of China's Electricity Consumption

10.21203/rs.3.rs-648522/v1 ◽

2021 ◽

Author(s):

Liqin Sun ◽

Youlong Yang ◽

Tong Ning ◽

Jiadi Zhu

Keyword(s):

Prediction Models ◽

Demand Forecasting ◽

Electricity Consumption ◽

Original Data ◽

Development Trend ◽

Statistical Hypothesis ◽

Grey Prediction ◽

New Information ◽

Proposed Model ◽

Grey Models

Abstract The grey prediction models of time series are widely used in demand forecasting because only limited data can be used to build the models and no statistical hypothesis is needed. In this paper, a grey power Markov prediction model (RGPMM(λ,1,1)) with time-varying parameters is proposed. This model is based on the principle of “new information priority”, combined with rolling mechanism and Markov theory, and the prediction residual error is modified to further improve the prediction accuracy. Compared with the classic grey models, the new model not only overcomes the inherent defect of poor adaptability to the original data, but also uses real-time information to better reflect the nonlinear characteristics of the original data, so it can be used to describe and predict the nonlinear development trend of things. In order to verify the validity and applicability of the model, the proposed model is used to forecast the total electric consumption in China. The experimental results show that the proposed model has a better prediction effect than other grey models. The proposed model is used to forecast China’s total electricity consumption in the next six years from 2018 to 2023.

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A variable-order fractional discrete grey model and its application

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210871 ◽

2021 ◽

pp. 1-14

Author(s):

Huang Meixin ◽

Liu Caixia

Keyword(s):

Electricity Consumption ◽

Grey Model ◽

Liaoning Province ◽

Model Parameters ◽

Variable Order ◽

Matrix Perturbation ◽

Application Range ◽

Whale Optimization ◽

Grey Models ◽

The Stability

Fractional order grey model is effective in describing the uncertainty of the system. In this paper, we propose a novel variable-order fractional discrete grey model (short for VOFDGM(1,1)) by combining the discrete grey model and variable-order fractional accumulation, which is a more general form of the DGM(1,1). The detailed modeling procedure of the presented model is first systematically studied, in particular, matrix perturbation theory is used to prove the validity in terms of the stability of the model, and then, the model parameters are optimized by the whale optimization algorithm. The accuracy of the proposed model is verified by comparing it with classical models on six data sequences with different forms. Finally, the model is applied to predict the electricity consumption of Beijing and Liaoning Province of China, and the results show that the model has a better prediction performance compared with the other four commonly-used grey models. To the best of our knowledge, this is the first time that the variable-order fractional accumulation is introduced into the discrete grey model, which greatly increases the prediction accuracy of the DGM(1,1) and extends the application range of grey models.

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Prediction of China’s Sulfur Dioxide Emissions by Discrete Grey Model with Fractional Order Generation Operators

Complexity ◽

10.1155/2018/8610679 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 13

Author(s):

Wei Meng ◽

Daoli Yang ◽

Hui Huang

Keyword(s):

Sulfur Dioxide ◽

Fractional Order ◽

Current Trend ◽

Grey Model ◽

Order System ◽

Fractional Operators ◽

Sulfur Dioxide Emissions ◽

Forecast Result ◽

Grey Models ◽

Improved Performance

Sulfur dioxide is an important source of atmospheric pollution. Many countries are developing policies to reduce sulfur dioxide emissions. In this paper, a novel prediction model is proposed, which could be used to forecast sulfur dioxide emissions. To improve the modeling procedure, fractional order accumulating generation operator and fractional order reducing generation operator are introduced. Based on fractional order operators, a discrete grey model with fractional operators is developed, which also makes use of genetic algorithms to optimize the modeling parameter r. The improved performance of the model is demonstrated via comparison studies with other grey models. The model is then used to predict China’s sulfur dioxide emissions. The forecast result shows that the amount of sulfur dioxide emissions is steadily decreasing and the policies of sulfur dioxide reduction in China are effective. According to the current trend, by 2020, the value of China’s sulfur dioxide emissions will be only 86.843% of emissions in 2015. Fractional order generation operators can be used to develop other fractional order system models.

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Ray of Hope: Hopelessness Increases Electricity Consumption for Lighting

PsycEXTRA Dataset ◽

10.1037/e509992015-259 ◽

2014 ◽

Author(s):

Ping Dong ◽

Xun (Irene) Huang ◽

Chen-Bo Zhong

Keyword(s):

Electricity Consumption

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Thermal/electric energy generation and CO2 production for greenhouse facilities

Power and Autonomous equipment ◽

10.32464/2618-8716-2019-2-3-141-151 ◽

2019 ◽

Vol 2 (3) ◽

pp. 141-151

Author(s):

O. E. Gnezdova ◽

E. S. Chugunkova

Keyword(s):

Carbon Dioxide ◽

Power Distribution ◽

Power Plants ◽

Electric Energy ◽

Electricity Consumption ◽

Vegetable Crops ◽

Carbon Dioxide Injection ◽

Electricity Rates ◽

Traditional Patterns ◽

Generation Power

Introduction: greenhouses need microclimate control systems to grow agricultural crops. The method of carbon dioxide injection, which is currently used by agricultural companies, causes particular problems. Co-generation power plants may boost the greenhouse efficiency, as they are capable of producing electric energy, heat and cold, as well as carbon dioxide designated for greenhouse plants.Methods: the co-authors provide their estimates of the future gas/electricity rates growth in the short term; they have made a breakdown of the costs of greenhouse products, and they have also compiled the diagrams describing electricity consumption in case of traditional and non-traditional patterns of power supply; they also provide a power distribution pattern typical for greenhouse businesses, as well as the structure and the principle of operation of a co-generation unit used by a greenhouse facility.Results and discussion: the co-authors highlight the strengths of co-generation units used by greenhouse facilities. They have also identified the biological features of carbon dioxide generation and consumption, and they have listed the consequences of using carbon dioxide to enrich vegetable crops.Conclusion: the co-authors have formulated the expediency of using co-generation power plants as part of power generation facilities that serve greenhouses.

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