基于稀疏自编码非线性自回归网络的负荷电价预测方法

Electricity load price forecasting based on sparse autoencoder nonlinear autoregressive network

准确的电价和负荷预测对现代电力系统至关重要,但由于电价与负荷之间存在较强的相关性,若不考虑其相互影响,将导致预测的精度下降。为了提高现有方法的预测准确性,在考虑价格与负荷关系的前提下,提出了一种基于深度递归神经网络的价格与负荷预测模型,即基于外部输入的稀疏自编码器的非线性自回归网络,其功能包括特征提取和预测。首先针对特征提取环节,对原有方法进行改进,提出了稀疏自编码器,可以大大提高特征提取的有效性。其次,利用非线性自回归网络进行电价和负荷预测。使用电力市场大数据ISONE和PJM进行仿真验证,与级联Elam网络相比,ESAENARX在负荷预测方面将平均绝对误差降低了16%,在价格预测方面降低了7%。

It is essential for modern power system to forecast electricity price and load accurately. However, due to the strong correlation between the electricity price and the load, if the mutual influence is not taken into account, the accuracy of the forecast will be reduced. In order to improve the prediction accuracy of existing methods, price and load relationship are considered and a deep recurrent neural networks model is proposed for price and load forecasting, that is sparse autoencoder nonlinear autoregressive network with exogenous inputs comprising of feature engineering and forecasting. Firstly, an efficient sparse autoencoder is proposed to improve the effectiveness of feature extraction by improving the original method. Secondly, the nonlinear autoregressive network is used to forecast the load and price. The ISONE and PJM big datas of power market are simulated and verified. Compared with cascaded Elman networks, sparse autoencoder nonlinear autoregressive network reduces the mean absolute error by 16% in load forecasting and 7% in price forecasting.