Short-term power load forecasting method based on multi-sensor data fusion
In order to control the stable operation of the power system,it is necessary to conduct research on short-term load fore-casting methods.Propose a short-term power load prediction method that integrates multi-sensor data.Firstly,use a two-dimension-al wavelet threshold method to denoise the power load data to improve data quality;Then select a self coding neural network to fuse and process the power load data collected by multiple sensors;Finally,based on the selected and fused data,a neural network power load prediction model is established to achieve short-term prediction of power loads.The experimental results show that the prediction method integrating multi-sensor data can effectively process data,improve data quality,and have high prediction accuracy.
data fusiontwo-dimensional wavelet threshold denoisingdata qualityprediction accuracy
万方数据
维普
