Information Mining Based on Seasonal and Trend Decomposition Using Loess for Non-Continuous EV Charging Prediction

With the widespread adoption of electric vehicles, predicting user charging consumption can enhance the operational efficiency of charging infrastructure. However, differences in user charging habits result in charging station operators obtaining data that is non-continuous and event-driven, lacking internal battery state information. This makes traditional methods difficult to apply directly. This paper explores how to accurately predict user charging consumption based on non-continuous observation data from charging stations. To this end, we propose a three-stage solution: (1) Design a method for segmenting the temporal sequence of users’ internal charging behavior based on statistical significance testing, enabling unsupervised recognition of homogeneous sequences of user behavior patterns; (2) establish a continuous-time reconstruction mechanism based on a physics-inspired power decay model to convert discrete homogenous sequences into equidistant daily sequences of charging consumption; (3) utilize seasonal and trend decomposition using Loess (STL) time-series decomposition to extract the component from the reconstructed sequence and input it as a feature into the Long Short-Term Memory (LSTM) prediction model. Through experimental validation using real charging data, the proposed method significantly enhances prediction performance, providing an effective solution for forecasting user charging consumption in actual charging stations.