Forecasting the EV charging load based on customer profile or station measurement?

Abstract

In this paper, forecasting of the Electric Vehicle (EV) charging load has been based on two different datasets: data from the customer profile (referred to as charging record) and data from outlet measurements (referred to as station record). Four different prediction algorithms namely Time Weighted Dot Product based Nearest Neighbor (TWDP-NN), Modified Pattern Sequence Forecasting (MPSF), Support Vector Regression (SVR), and Random Forest (RF) are applied to both datasets. The corresponding speed, accuracy, and privacy concerns are compared between the use of the charging records and station records. Real world data compiled at the outlet level from the UCLA campus parking lots are used. The results show that charging records provide relatively faster prediction while putting customer privacy in jeopardy. Station records provide relatively slower prediction while respecting the customer privacy. In general, we found that both datasets generate comparable prediction error.

Prediction of Operating Abnormality Rate of Charging Pile Based on Generalized AR(q) Combined Regression

“…The operation and maintenance impact index combines with the weather impact index to form a comprehensive impact index to reflect the total effect of operation and maintenance and weather on the abnormality duration of the charging pile and it is shown as follows with the regression model: (6) The sum of the first three terms of model (6) is called the comprehensive index :…”

“…If there are q anomalies of the i-th charging pile before the t-th day, then the generalized AR(q) model (1) can be used to predict its abnormality duration; If the number of abnormality occurred before t days is less than q, then it is unable to use (1) for predictions. However, the comprehensive prediction model (6) or the comprehensive prediction index (7) can be used to predict the abnormality duration. In fields related to energy prediction, the use of combined prediction techniques based on time series prediction is a relatively practical and effective choice.…”

“…In fields related to energy prediction, the use of combined prediction techniques based on time series prediction is a relatively practical and effective choice. We will use the generalized AR(q) model (1) as the basis and combine the comprehensive prediction index model (6) to predict the abnormality duration. The prediction model is constructed as follows: (8) Once the coefficient estimates of (1) and (6) are obtained, the prediction formula can be written as: (9) Based on the prediction model (8) or (9), the predicted value of the abnormality duration of the charging pile per day can be obtained.…”

Allocation Method for Mobile Charging Vehicles Based on the Spatial Charging Demand

“…The draft of the construction planning about the basic charging facilities has been optimistically made by National Energy Administration of China with the result that the production and sales for the new-energy vehicles will be more than five million by 2020 [7]. If 1 vehicle is averagely allocated to 1 slow charger and 0.2 fast charger, there will be 5 million slow chargers and 1 million fast chargers in the future in China [8], [9]. However there are many charging stations has been found to be idle for most of the time which becomes a large waste of investment [10].…”

Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm

“…Electric buses (e-buses) have developed quickly with the burgeoning construction of low-carbon cities [2]. As important supporting facilities, charging stations bring new challenges to optimal dispatching and safe operation of the power grid due to great volatility, randomness and intermittence of the load [3]. Therefore, it is of great significance to conduct research on load characteristics analysis and short-term load forecasting.…”