Short-Term Load Forecasting Based on Improved VMD and KELM

Abstract

To address the problem of low accuracy of short-term load prediction in the past, this thesis uses a prediction model with neural network LSTM as the main body and constructs an adaptive and dynamically adjusted rolling load prediction model to meet our load prediction needs through VMD decomposition, kernel limit learning machine, and correlation analysis techniques. To reduce the maximum load error and the overall result error, a correction algorithm is proposed to improve the VMD to minimize the impact of load decomposition; after that, the model is used to analyze a load of a day in a region to verify the superiority of the prediction model. The example shows that the load forecasting method can help to make more accurate predictions of short-term loads.