基于误差补偿的分数阶灰色模型对四川省煤炭类能源消费的预测分析

The predict and analysis on the coal category energy consumption in Sichuan province based on a fractional grey model of error compensation

为了对能源需求进行更精确的预测,基于分数阶灰色模型和反向传播神经网络,建立基于误差补偿的分数阶灰色模型。随后利用河北省历年电力消费数据检验模型的精度,并将其与传统分数阶灰色模型作对比。最后对四川省煤炭类能源需求量进行了预测分析。结果表明,基于误差补偿的分数阶灰色模型具有更高的精确度与有效性,反向传播神经网络对分数阶灰色模型的误差补偿作用明显,该模型有效地模拟了原始数据的变化趋势,并预测出煤炭类能源的消费量将呈下降后趋于平稳的趋势。

In order to predict energy demand more accurately, a fractional-order grey model based on error compensation is established based on fractional-order grey model and back-propagation neural network. The accuracy of the model is tested with the power consumption data of Hebei province, and compared with the traditional fractional grey model. The coal energy demand in Sichuan province is forecasted and analyzed. Results show that the fractional order grey model based on the error compensation has higher accuracy and effectiveness, back propagation neural network has an obvious effect on error compensation of the fractional order grey model.The model effectively simulates the change trend of raw data,predicts that the coal type energy consumption will be leveled off after a downward trend.