A Grey Prediction Approach to Forecasting Energy Demand in China

Dechang Pi · Qin Xu

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Grey System Theory Applications Forecasting Techniques and Applications Energy Load and Power Forecasting

Abstract

Energy demand forecasting plays an important role in decision making. A mathematical model known as grey model GM(1,1) has been, herewith, employed successfully in the estimation of energy demand. In order to improve the forecast accuracy, the original GM(1,1) models are improved by using three methodologies of the 3-points average technology and the residual modification. This method takes into account the general trend series and random fluctuations about this trend. Two experiments were carried out respectively on the electricity demand and energy production from 1984 to 2006 in China to demonstrate the effectiveness of our approach. Furthermore, this improved grey forecasting model was used to forecast China's electricity demand and energy production, which shows that the modified forecasting model is more reliable and has a higher forecast accuracy than the GM(1,1). The forecasted results indicate that China's final energy demand will increase rapidly in the period 2007–2015. The results provide scientific basis for the planned development of energy supply in China.

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References

1988China statistical yearbook 1982Control problems of grey systems 2004Energy consumption in China: past trends and future directions 2007Grey prediction with rolling mechanism for electricity demand forecasting of Turkey 2003Applications of improved grey prediction model for power demand forecasting 2006A trigonometric grey prediction approach to forecasting electricity demand 2004Application of grey model GM(1, 1) to vehicle fatality risk estimation 2007Ant colony optimization approach to estimate energy demand of Turkey

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2019Energy Sector Development: System Dynamics Analysis 2020Energy demand forecasting using a novel remnant GM(1,1) model 2022Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China 2021An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting 2021How to Predict Energy Consumption in BRICS Countries?2020A Multivariate Grey Prediction Model Using Neural Networks with Application to Carbon Dioxide Emissions Forecasting

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