Abstract
It is of great significance to be able to accurately predict the time series of energy data. In this paper, based on the seasonal and nonlinear characteristics of monthly and quarterly energy time series, a new optimized fractional grey Holt–Winters model (NOFGHW) is proposed to improve the identification of the model by integrating the processing methods of the two characteristics. The model consists of three parts. Firstly, a new fractional periodic accumulation operator is proposed, which preserves the periodic fluctuation of data after accumulation. Secondly, the new operator is introduced into the Holt–Winters model to describe the seasonality of the sequence. Finally, the LBFGS algorithm is used to optimize the parameters of the model, which can deal with nonlinear characteristics in the sequence. Furthermore, in order to verify the superiority of the model in energy prediction, the new model is applied to two cases with different seasonal, different cycle, and different energy types, namely monthly crude oil production and quarterly industrial electricity consumption. The experimental results show that the new model can be used to predict monthly and quarterly energy time series, which is better than the OGHW, SNGBM, SARIMA, LSSVR, and BPNN models. Based on this, the new model demonstrates reliability in energy prediction.
Highlights
The experimental results show that the new model can be used to predict monthly and quarterly energy time series, which is better than the OGHW, SNGBM, SARIMA, LSSVR, and BPNN models
In order to compare the performance of the model more clearly, this paper adopts the following five methods to evaluate the effect of the model, which are average percentage error (APE), average absolute percentage error (MAPES, MAPSP), root mean
In order to compare the performance of the model more clearly, this paper adopts the following five methods to evaluate the effect of the model, which are average percentage error (APE), average absolute percentage error (MAPES, MAPSP), root mean square error (RMSES, RMSEP)
Summary
As the cornerstone of economic development and industrial progress, energy plays a significant role. Energy Bureau, China’s energy consumption and production have been on the rise in recent years. Not all energy has the characteristic of inexhaustible; all countries in the world advocate sustainable development strategy. In this context, energy prediction must be made. Energy forecasting is the advance planning of the future energy market, an important means to maintain the balance between supply and demand in the market, reduce the waste of resources, and provide technical support for the implementation of sustainable development strategy. Influenced by many uncertain factors, monthly and quarterly energy time series often show more data characteristics, such as seasonality and nonlinearity. Energy prediction has been a hot issue, and its research is of great
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