Forecasting the Energy Consumption of China’s Manufacturing Using a Homologous Grey Prediction Model

Abstract

Abstract:With the rapid development of China's manufacturing, energy consumption has increased rapidly, and this has become a major bottleneck affecting the sustainable development of China's economy. This paper deduces and constructs a homologous grey prediction model with one variable and one first order equation (HGEM(1,1)) for forecasting the total energy consumption of China's manufacturing based on the Grey system theory. Both parameter estimation (PE) and the deduction of the final restored expression (FRE) of the HGEM(1,1) model are all from the time response expression of the whitenization differential equation, which solves the 'non-homologous' defects of PE and FRE with traditional grey prediction models. HGEM(1,1) has good performance and can unbiasedly simulate a homogeneous/non-homogeneous exponential function sequence and a linear function sequence. Then, the HGEM(1,1)model is used to simulate and forecast the total energy consumption of China's energy manufacturing, and the results show that the comprehensive performance of this model is much better than that of the classic Grey Model with one variable and single order equation, GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, we forecast the total energy consumption of China's manufacturing industry during the years 2018-2024. The results show that the total energy consumption in China's manufacturing is slowing down but is still too large. For this, some measures, such as optimizing the manufacturing structure and speeding up the development and promotion of energy saving and emission reduction technologies, to ensure the effective supply of energy in China's manufacturing industry are suggested.

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“…The GM(1,1) model is based on the assumption that the original data sequence obeys the exponential distribution [47,61]. However, the practical data sequence often exhibits the characteristic of approximately inhomogeneous exponential growth [48,49].…”

“…Although the GM(1,1) model has been widely applied as mentioned above, the theoretical system of grey system is immature due to short history, and improvement of prediction precision of GM(1,1) model is strategically important in practice in a certain situation. Distribution of the original data sequence is an important factor that affects the prediction accuracy of GM(1,1) model [47][48][49]. Furthermore, in the traditional GM(1,1) model, the first data of the data sequence is not involved in the modelling procedure, which reduces the data utilization efficiency and induces prediction error [50].…”

“…The GM(1,1) model is the first order Grey model with only one variable; the first "1" denotes the "first order", and the latter "1" means the "univariate". The model is suitable in the situation that the data sequence satisfies or basically satisfies exponential growth and the growth speed is relatively slow [47]. The GM(1,1) model is featured by high computational efficiency and requirement of only one parameter fitting the model.…”

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“…Deng [14] built the grey system theory-GM (1,1) which has been a typical forecasting model, and widely used in energy [15,16]. A large number of scholars have carried out further research on GM (1,1) model in recent years [17,18].…”

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“…In the predictive model, the GM (1,1) model is the most classic. At present, the grey prediction model and its improved model have been widely used in various aspects of society, such as energy [14,15], agriculture [16], technology [17], environment [18] and medical [19]. In view of this, the majority of experts and scholars are constantly improving and optimizing it.…”