Short-term Power Load Forecasting Method Based on Variational Modal Decomposition for Convolutional Long-short-term Memory Network

The power load sequence is complicated and easily affected by multiple external factors, making it difficult to anticipate with accuracy. A parallel forecasting method of short-term power load combining variational modal decomposition (VMD) and convolutional neural network and long short-term memory network (CNN-LSTM) is proposed to address the problem. Firstly, VMD is adopted to decompose the load data into various intrinsic mode functions (IMF) with strong regularity and residual error; Secondly, the obtained components are input into the corresponding CNN-LSTM hybrid prediction network to obtain each initial prediction value, and combine this value with the correlation factor feature set obtained by combining climate, date type, etc. to further obtain the revised prediction value; Finally, the revised prediction values of each component are superimposed to obtain a complete prediction result. According to the simulation on the actual load data, the average relative error of daily load forecasting can be reduced by 2.18% after taking into about the relevant external factor features set. In addition, compared with several conventional load forecasting methods, the effectiveness and feasibility of the proposed method can be verified.