Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China

Wu, Lifeng; Gao, Xiaohui; Xiao, Yanli; Yang, Yingjie; Chen, Xiangnan

doi:10.1016/j.energy.2018.05.147

Cited by 190 publications

(78 citation statements)

References 43 publications

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“…The possible limitation of the proposed approach is as follows. Inherited from the limitation of grey forecasting as indicated in a lot of previous literature (e.g., [7,20,21]), the proposed method includes the grey forecasting method that adopts the least-squares method in estimation, so the predictions of the proposed method may be biased when the data samples have a lot of noise or show a sudden peak/valley owing to some sudden events or external factors. For example, when some country or region starts to implement a series of regulations and methods to prohibit or reduce CO 2 emissions in some year, the data that year may have a large drop.…”

Section: Discussionmentioning

confidence: 99%

“…This method has already been used for forecasting the amount of CO 2 emissions and is applied in various fields. As indicated from a lot of previous literature (e.g., [7,20,21]), the grey forecasting method enjoys the following advantages: (1) this method is easily operated; (2) this method requires only a small amount of data samples to make accurate forecasts (in general, only four or more samples are required); (3) no series distribution is supposed in advance. However, it has the following flaws: (1) this method includes the least-squares method, which may produce biased forecast results when the system has a lot of noise; (2) this method is not suitable for making long-run forecasts.…”

Section: Grey Forecastingmentioning

confidence: 99%

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Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

Lin

Liu

2018

Energies

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Development of technology and economy is often accompanied by surging usage of fossil fuels. Global warming could speed up air pollution and cause floods and droughts, not only affecting the safety of human beings, but also causing drastic economic changes. Therefore, the trend of carbon dioxide emissions and the factors affecting growth of emissions have drawn a lot of attention in all countries in the world. Related studies have investigated many factors that affect carbon emissions such as fuel consumption, transport emissions, and national population. However, most of previous studies on forecasting carbon emissions hardly considered more than two factors. In addition, conventional statistical methods of forecasting carbon emissions usually require some assumptions and limitations such as normal distribution and large dataset. Consequently, this study proposes a two-stage forecasting approach consisting of multivariable grey forecasting model and genetic programming. The multivariable grey forecasting model at the first stage enjoys the advantage of introducing multiple factors into the forecasting model, and can accurately make prediction with only four or more samples. However, grey forecasting may perform worse when the data is nonlinear. To overcome this problem, the second stage is to adopt genetic programming to establish the error correction model to reduce the prediction error. To evaluating performance of the proposed approach, the carbon dioxide emissions in Taiwan from 2000 to 2015 are forecasted and analyzed. Experimental comparison on various combinations of multiple factors shows that the proposed forecasting approach has higher accuracy than previous approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Grey Forecastingmentioning

confidence: 99%

Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

Lin

Liu

2018

Energies

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“…(20) with the given parameters α,β,γ , the parameters (α, β, γ) of FAGMO(1,1,k) obtained by Eqs. (4) and(25) to(27) satisfy the relationship α = α,β = β,γ = γ, and the predicted values x (0) (1),x (0) (2), . .…”

mentioning

confidence: 94%

“…Energy consumption prediction constitutes an important aspect of energy policies for countries globally, particularly developing countries such as China, where the energy consumption structure is changing at a rapid speed. Numerous models have been introduced for forecasting energy consumption, such as dynamic causality analysis [1], nonlinear and asymmetric analysis [2], time-series analysis [3,4], machine learning models [5], the coupling mathematical model [6,7,8], autoregressive distributed lag model [9], hybrid forecasting system [10,11], machining system [12], fuzzy systems [13], LEAP model [14,15], TIMES model [16,17], NEMS model [18,19] and grey model [20,21,22,23,24,25,26,27,28]. Among these prevalent methods, simple linear regression, multivariate linear regression, and time-series analysis are often significant in accurately demonstrating the phenomena of long-term trends.…”

Section: Introductionmentioning

confidence: 99%

Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption

Wu¹,

Ma²,

Zeng³

et al. 2018

Energy

106

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At present, the energy structure of China is shifting towards cleaner and lower amounts of carbon fuel, driven by environmental needs and technological advances. Nuclear energy, which is one of the major low-carbon resources, plays a key role in China's clean energy development. To formulate appropriate energy policies, it is necessary to conduct reliable forecasts. This paper discusses the nuclear energy consumption of China by means of a novel fractional grey model FAGMO(1,1,k). The fractional accumulated generating matrix is introduced to analyse the fractional grey model properties. Thereafter, the modelling procedures of the FAGMO(1,1,k) are presented in detail, along with the transforms of its optimal parameters. A stochastic testing scheme is provided to validate the accuracy and properties of the optimal parameters of the FAGMO (1,1,k). Finally, this model is used to forecast China's nuclear energy consumption and the results demonstrate that the FAGMO(1,1,k) model provides accurate prediction, outperforming other grey models.

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“…The classical GM(1, 1) model has been generalized to other effective grey forecasting models, including NGM(1, 1, k) (Cui et al, 2009), DGM(1, 1) (Hu et al, 2009), NGM(1,N ) (Wang & Ye, 2017), and CFGM (Ma et al, 2019c). These grey models have been successfully applied in the environment (Wu et al, 2018a), economy (Yin et al, 2018), energy (Wu et al, 2019) and other related fields (Wang et al, 2018a;Duan et al, 2019). From the idea of GM (1, 1) modeling, it is the least-squares modeling method that follows the law of accumulated grey-index.…”

mentioning

confidence: 99%

Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity

Xia

Huang

et al. 2020

Journal of Cleaner Production

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Wind energy makes a significant contribution to global power generation. Predicting wind turbine capacity is becoming increasingly crucial for cleaner production. For this purpose, a new information priority accumulated grey model with time power is proposed to predict short-term wind turbine capacity. Firstly, the computational formulas for the time response sequence and the prediction values are deduced by grey modeling technique and the definite integral trapezoidal approximation formula. Secondly, an intelligent algorithm based on particle swarm optimization is applied to determine the optimal nonlinear parameters of the novel model. Thirdly, three real numerical examples are given to examine the accuracy of the new model by comparing with six existing prediction models. Finally, based on the wind turbine capacity from 2007 to 2017, the proposed model is established to predict the total wind turbine capacity in Europe, North America, Asia, and the world. The numerical results reveal that the novel model is superior to other forecasting models. It has a great advantage for small samples with new characteristic behaviors.Besides, reasonable suggestions are put forward from the standpoint of the practitioners and governments, which has high potential to advance the sustainable improvement of clean energy production in the future. . Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity.

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Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China

Cited by 190 publications

References 43 publications

Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption

Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity

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