一种改进Transformer的电力负荷预测方法

An Improved Power Load Forecasting Method Based on Transformer

负荷预测是电网系统中很多应用的关键部分,具有重要作用。然而,由于电网负荷的非线性、时变性和不确定性,使得准确预测负荷具有一定的挑战。充分挖掘负荷序列的潜在特征是提升预测准确率的关键。文中认为在特征提取时应该充分利用负荷序列的位置信息、趋势性、周期性和时间信息,同时还应构建更深层次的神经网络框架进行特征挖掘。因此,提出了基于特征嵌入和Transformer框架的负荷预测模型。该模型由特征嵌入层、Transformer层和预测层组成。在特征嵌入层,模型首先对历史负荷的位置信息、趋势性、周期性和时间信息进行特征嵌入,然后再与天气信息进行融合,得到特征向量。Transformer层则接受历史序列的特征向量并挖掘序列的非线性时序依赖关系。预测层通过全连接网络实现负荷预测。从实验结果来看,文中模型的预测性能优于对比模型,体现了该模型的可行性和有效性。

Load forecasting is a key part of many power grid applications and plays an important role. However, the nonlinearity, time-varying characteristics and uncertainty of grid load are the challenging to accurate load prediction, therefore, it plays a vital role to improve prediction accuracy by fully mine potential characteristics of load sequence. It is considered that the position information, trend, periodicity and time information of the load sequence should be fully utilized in feature extraction, and a neural network framework at a deeper level should be constructed for feature mining. For this reason, a load forecasting model based on feature embedding and Transformer framework was proposed, and the proposed model was composed by a feature embedding layer, a Transformer layer and a prediction layer. In the feature embedding layer, firstly,the location information, trend, periodicity and time information of the historical load were embedded into a characteristic vector, and then its output feature vector was blended with meteorology information to obtain the feature vector. The Transform layer accepted the feature vector of historical series and mined the temporal nonlinear dependence hidden in the sequence based on the obtained feature vectors of load sequence.Through the fully connected network the prediction layer implemented the load forecasting. Experimental results show that the forecasting performance of the proposed model is better than that of the comparative model, thus both feasibility and availability of the proposed model are verified.