To address the challenge that connected and automated electric vehicle (EV) charging operation requires short-term load forecasts that preserve the current operating level while accurately capturing local ramps and peaks under strict forecast time information constraints, this paper proposes a forecast-time-safe periodicity-aware residual correction (PARC) framework. The primary experiment is a controlled benchmark on a 60-day processed distribution load proxy series, while a charging load series reconstructed from public Boulder, Colorado, EV charging transactions is used as a secondary traceable validation case. Rather than directly predicting the next load value, PARC uses the persistence forecast as the local operating state anchor and learns only the residual correction from admissible lag, rolling statistical, ramp, daily/weekly memory, and cyclic time features. This design enables a controlled comparison between direct load prediction and residual correction under the same feature boundary. In the primary proxy-series setting, PARC-HistGBR achieves a test mean absolute percentage error (MAPE) of 1.527% and a root mean square error (RMSE) of 37.051 kW, outperforming persistence, a validation-selected seasonal blend, same-feature direct tree learners, long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM). Additional XGBoost, LightGBM, and CatBoost residual variants, together with Seasonal-ETS and SARIMA-daily statistical baselines, support the interpretation that the residual target formulation, rather than one specific learner, accounts for the main gain. Rolling-origin checks, day-block bootstrap intervals, Diebold–Mariano tests, and Wilcoxon signed-rank tests provide supporting evidence within the short-data setting. In the Boulder EV validation case, the model ranking is metric-dependent, with simple persistence remaining strong for percentage metrics and residual/tree models improving selected absolute error metrics. The results indicate that PARC is useful as an auditable forecast-time-safe residual benchmarking framework for connected and automated EV charging operation; they should not be interpreted as evidence of universal superiority on fully traceable EV-rich feeders.
Yaqi Liang (Mon,) studied this question.