A Comparative Study of Short-Term Electric Vehicle Load Forecasting Using Data-Driven Multivariate Probabilistic DeepAR Approach
With the surge of electric vehicles (EVs) and consequently the increase in power consumption, the power grid is facing many new challenges. Charging load forecasting remains one of the key challenges, that if not effectively scheduled, it may result in instability and quality-related issues in power systems. In recent years, numerous load forecasting techniques using machine learning and deep learning were proposed for predictions covering both commercial and household demands. However, there are very few studies that employed these methods to predict EV charging load behavior. This thesis proposes a multivariate RNN-based deep learning framework to forecast the short-term data-driven EV charging loads on two specific datasets for residential and workplace usage. In this research, a few popular deep learning models have been comparatively investigated to evaluate the forecasting performance of the proposed multivariate DeepAR model, a recurrent neural network-based model, as well as its univariate model on the historical charging data with exogenous variables. The 5-tuples input data used in this research include charging start time, duration of charging, charging load, time of use electricity price, and weekdays/weekends that were collected from three different locations and categorized into residential and workplace/parking lot scenarios. The short-term load forecasting algorithm in this study has been utilized multi-step daily horizons as one, three, seven and fifteens days ahead for the prediction window. Numerical results show that the multivariate DeepAR algorithm persists with manifestly higher stability and accuracy over multi-step daily prediction horizons. Its symmetric mean absolute percentage error (SMAPE) and mean absolute scaled error (MASE) are maintained at 1.9% and 4.95%, respectively, and outperform by a significant margin all other investigated deep learning and statistical models on the provided EV historical charging datasets. Eventually, the proposed framework can be further employed to formulate a more complex approach regarding charging load management at charging stations to maximize the load factor as well as balancing and flattening peak loads on the grid system
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Faculty of Graduate Studies and Research, University of Regina