A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors

Wang, Zheng‐Xin; Qin, Li; Pei, Ling-Ling

doi:10.1016/j.energy.2018.04.155

Cited by 223 publications

(98 citation statements)

References 41 publications

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“…To obtain the WF-AGO sequence, we have to determine the parameters r and λ in advance. To get the solution of the WFGM(1,1) model, the development coefficient a and the grey input action b need to be fixed in the linear differential equation (11). Fortunately, all these parameters can be obtained by solving two optimization problems.…”

Section: Parameters Estimation and Computationalmentioning

confidence: 99%

“…Finally, applying the rst order-inverse accumulated generating operator (1-IAGO), one can get the tted value X (0) of X (0) and obtain the future trend. In the past few decades, it has been widely utilized in a great deal of engineering applications, such as the global integrated circuit industry prediction [7], the vehicle fatality risk estimation [8], the short-term freeway tra c parameter prediction [9], the fashion retailing prediction problem [10], the electricity consumption forecasting [11], and the natural gas consumption forecasting [12,13]. To improve the prediction accuracy of the GM(1,1) model, many researchers have carried out a lot of works from di erent aspects, such as nding new accumulation generating operators [14][15][16][17][18][19], constructing more accurate background value formula [20,21], choosing parameter optimization methods [22], improving initial guess [23], and reducing residuals based on Fourier analysis and Markov chain [9,20].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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Based on the ideas of the new information priority principle and the fractional-accumulation generating operator, in this paper we propose a novel weighted fractional GM(1,1) (WFGM(1,1)) prediction model. In the new model, the original sequence is first transformed by using the weighted fractional-accumulation generating operator, which involves two parameters. With special choices of these parameters, the proposed WFGM(1,1) model reduces to the classical GM(1,1) model and the fractional GM(1,1) (FGM(1,1)) model, as well as the new information priority GM(1,1) (NIPGM(1,1)) model studied recently. Stability property of the WFGM(1,1) model is studied in detail. In practice, the quantum particle swarm optimization algorithm is adopted to choose the quasi-optimal parameters for the new model so as to get the best fitting accuracy. Finally, four numerical examples from different practical applications are present. Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.

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Section: Parameters Estimation and Computationalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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“…Meanwhile, electricity is also regarded as one of the most significant driving forces of economic development and is deemed essential in our daily life [1][2][3]. Therefore, prediction of electricity consumption has become urgent and important for a country or region [4][5][6]. Establishment of an accurate and reliable forecasting model for electricity consumption, which could provide valuable information for electricity system operators to formulate policies and plans of electricity [7], is vital for the management of power system.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Xu et al [29] proposed the IRGM(1,1) model, by optimizing the time response function, to forecast the electricity consumption in China, and the IRGM(1,1) model can significantly promote forecast accuracy according to comparison of the experimental results. Wang et al [5] applied a seasonal grey model (SGM(1,1) model), which is based on the accumulation operators generated by seasonal factors, to forecast the primary industrial electricity consumption in China from 2010 to 2016, and the prediction accuracy of SGM(1,1) model outperforms the original GM(1,1) model and some improved grey models. Hamzacebi and Es [46] proposed an optimized GM(1,1) model to predict the total electric energy demand of Turkey from 2013 to 2025, and the superiority of the optimized GM(1,1) model is significant when compared with other forecasting models.…”

Section: Background and Motivationmentioning

confidence: 99%

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Zhang

2018

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Prediction of electricity consumption plays critical roles in the economy. Accurate electricity consumption forecasting is essential for policy makers to formulate electricity supply policies. However, limited data and variables generally cannot provide sufficient information to gain satisfactory prediction accuracy. To address this problem, we propose a novel improved grey forecasting model, which combines data transformation for the original data sequence and combination interpolation optimization of the background value of the GM(1,1) model, and is therefore named DCOGM(1,1). To evaluate the simulation and prediction performance of DCOGM(1,1), two case studies are carried out. In addition, the results show that DCOGM(1,1) outperforms most existing improved grey models in terms of forecasting accuracy. Finally, DCOGM(1,1) is employed to predict the total electricity consumption of Shanghai City in China from 2017 to 2021. In addition, the results suggest that DCOGM(1,1) performs well compared with the traditional GM(1,1) model and other grey modification models in this context and Shanghai's electricity consumption will increase stably in the following five years. In summary, DCOGM(1,1) proposed in our study has competent exploration and exploitation ability, and could be utilized as an effective and promising tool for short-term planning for other forecasting issues with limited source data as well.The forecast accuracy of electricity consumption is affected by many factors, such as economic development [8], population [9], power facilities [10] and climate factors [11], and thus the data sequence of electricity consumption often presents to be highly nonlinear, stochastic, time-changeable,

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“…It quantifies the concept of system information sampling, conceptualizes the quantitative model, and finally optimizes the model to predict some unknown data. The GM (1, 1) model has no specific requirements for sample size, and it can be used to research the future time distributions for specific time intervals [55]. Li et al consider that the GM (1, 1) model is usually used to predict samples with a small amount of data [56].…”

Section: Gm (1 1) Forecasting Modelmentioning

confidence: 99%

Analyzing and Forecasting Energy Consumption in China’s Manufacturing Industry and Its Subindustries

2018

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In the context of new industrialization, the energy problem being experienced by the manufacturing industry has aroused social concerns. This paper focuses on the energy use of 27 subindustries in China’s manufacturing industry and it develops an energy consumption index for 1994–2015. Subsequently, the method of grey relational analysis is used, with the full period divided according to years in which change points occur. The empirical analysis indicates that the energy consumption indexes generally exhibit a declining trend. Using the grey model (GM (1,1)) to forecast the index indicates a continued downward trend up to 2025 for energy-intensive industries, which is a more optimistic scenario than the trend forecast for the whole manufacturing sector. Thus, these energy-intensive industries do not drag down the performance of the whole manufacturing industry in regard to energy intensity. In future, more attention should be paid to energy-saving efforts by nontraditional high-energy-consuming industries. Although the results show that energy efficiency is improving in China, total annual consumption is rising rapidly. Therefore, the industry needs to continue to strengthen independent innovation and improve the efficiency of new energy use. The Chinese government should formulate feasible long-term plans to encourage enterprises to save energy.

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A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors

Cited by 223 publications

References 41 publications

A Novel Weighted Fractional GM(1,1) Model and Its Applications

A Novel Weighted Fractional GM(1,1) Model and Its Applications

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Analyzing and Forecasting Energy Consumption in China’s Manufacturing Industry and Its Subindustries

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