Short-term Power Load Forecasting Based on Multi-view Temporal Features
External factors exhibit periodic influences on power load,and these impacts are directly reflected in load val-ues.Drawing inspiration from the concept of multi-perspective representation learning,this study leverages various per-spectives of historical load forecasting values as hidden representations of external factors.By extracting features from his-torical electricity load data and dividing the load into three time perspectives-minutes,hours,and days-tailored neural net-work models are employed for feature extraction based on these different time perspectives.Furthermore,a multi-per-spective feature fusion module is introduced,amalgamating information across different time scales to enhance load fore-casting accuracy.Empirical results demonstrate that the proposed method exhibits superior predictive performance on a power load dataset from a specific region in Southwest China.In comparison to models solely considering a single time per-spective,the proposed approach achieved reductions in MAE and MSE of 12.21%and 11.12%,respectively.
short-term power load forecastingmulti-view temporal featuremulti-feature fusion
NETL
NSTL
万方数据
