A novel hybrid deep neural network model for short-term electricity price forecasting
A novel hybrid deep neural network model for short-term electricity price forecasting
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DOI：
10.1002/er.5945
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发表时间：
2020-09-09
影响因子：
4.6
通讯作者：
Chen, Hsin-Chuan
中科院分区：
文献类型：
--
作者：
Huang, Chiou-Jye;Shen, Yamin;Chen, Hsin-Chuan
无处不在的电力物联网从根本上说是一个物联网，但重点是电力系统。能够准确地预测这些价格可能有助于确定客户的需求和电力生产商对电网的有效监管。它还可以帮助电力交易商管理风险，做出正确的决策，并获得更多的利益。本文提出了一种新的混合模型用于短期电价预测。该模型由三种算法组成：变分模式分解（VMD）;卷积神经网络（CNN）和门控递归单元（GRU）。为方便起见，这被称为SEPNet。由于电价时间序列存在季节性差异，因此将全年电价数据划分为季节。VMD算法用于将复杂的电价时间序列分解为具有不同中心频率的本征模态函数（IMF）。CNN用于进一步提取VMD域中所有固有模型函数的时域特征。然后，GRU被用来处理和学习CNN提取的时域特征，从而得到最终的预测。与LSTM、CNN、VMD-CNN、BP、VMD-ELMAN等五种模型进行了比较。结果表明，该模型具有最佳的性能，使用VMD可以将四季的平均绝对百分比误差（MAPE）和均方根误差（RMSE）分别提高84%和81%。在SEPNet模型中添加GRU进一步分别将MAPE和RMSE提高了19%和25%。包括CNN和VMD-CNN，这表明所提出的模型具有最好的性能。四个季节平均值的MAPE和RMSE分别为0.730%和0.453。这证实了SEPNet模型预测短期电价的可行性和较高的准确性。
A Ubiquitous Power Internet of Things is fundamentally an Internet of Things, but focused upon power systems. Being able to predict these prices accurately may help with the identification of customer needs and the effective regulation of the power grid by power producers. It may also help electric power traders to manage risks, make correct decisions, and obtain more benefits. In this paper, a novel hybrid model is proposed for short-term electricity price prediction. The model consists of three algorithms: Variational Mode Decomposition (VMD); a Convolutional Neural Network (CNN); and Gated Recurrent Unit (GRU). This is called SEPNet for convenience. The annual electricity price data is divided into seasons because of seasonal differences in the time series of electricity prices. The VMD algorithm is used to decompose the complex time series of electricity prices into intrinsic mode functions (IMFs) with different center frequencies. The CNN is used to further extract the time-domain features for all the intrinsic model functions in the VMD domain. The GRU is then employed to process and learn the time-domain features extracted by the CNN, leading to the final prediction. A comparison is made with five models, such as LSTM, CNN, VMD-CNN, BP, VMD-ELMAN. The results showed that the proposed model had the best performance, and it was found that using VMD can improve the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for the four seasons by 84% and 81%, respectively. The addition of GRU in the SEPNet model further improved the MAPE and RMSE by 19% and 25%, respectively. Including CNN and VMD-CNN, that shows that the proposed model has the best performance. The MAPE and RMSE for the four seasonal averages are 0.730% and 0.453, respectively. This confirms that the SEPNet model has the feasibility and high accuracy to predict short-term electricity prices.