EV charging load forecasting model mining algorithm based on hybrid intelligence

Precise EV charging load forecasting plays a critical role in optimizing resource allocation and facilitating economic operation and energy management of EV charging stations, as well as supporting the economic dispatch of the power grid for efficient and effective utilization of resources. Existing EV charging load forecasting models have highly restrictive load data requirements and thus have practical limitations in two fold: first, these models are a black box and cannot provide a quantitative reference for later analysis affecting EV charging load forecasting; and second, they fail in considering the impact of noisy data caused by uncontrollable factors (data collection failures, human errors, and network attacks, etc.) on the accuracy of EV charging load forecasting. To address the above issues, we propose a novel EV charging load forecasting model mining based on gene expression programming (CFMM-GEP) by fusing noisy load processing. This will tackle three-fold ideas: (1) the charging load dataset with abnormal data is reconstructed based on the Auto-Encoder, and the support vector machine -based abnormal detection algorithm is proposed; (2) a quantitative model for EV charging load forecasting based on gene expression programming is constructed. Extensive experiments are carried out on four open-source charging load datasets. Experimental results indicate that the superiority of our proposed CFMM-GEP model over 6 state-of-the-art models in terms of MAP E, RMSE, MAE, and R2.

Automated summary

Precise EV charging load forecasting is crucial for optimizing resource allocation and economic operation of EV charging stations. Existing models have limitations in terms of data requirements and handling noisy data. To address these issues, we propose a novel model based on gene expression programming that incorporates noise load processing for improved accuracy.