Abstract
Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants.
Highlights
Energy is necessary for the sustainable development and economic prosperity of a country [1], and this is evidenced by the fact that energy demand has emerged as an important economic index in recent years
This paper develops a grey forecasting model called the genetic-algorithm-based remnant GM(1,1) model (GARGM(1,1)) with sign estimation that delivers high prediction accuracy
The results show that the GARGM(1,1) model can outperform other variants of the remnant GM(1,1) model
Summary
Energy is necessary for the sustainable development and economic prosperity of a country [1], and this is evidenced by the fact that energy demand has emerged as an important economic index in recent years. A forecasting method is needed that can work with small samples without making statistical assumptions to construct an energy demand prediction model [10, 11].
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