Battery Swapping Demands Forecast for Electric Bicycles Based on Data-driven

被引：0

作者：

Shuai C.-Y. ^{[1
]}

Yang F. ^{[1
]}

Ouyang X. ^{[1
]}

Xu G. ^{[1
]}

机构：

[1] School of Transportation Engineering, Kunming University of Science and Technology, Kunming

来源：

Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2021年 / 21卷 / 02期

基金：

中国国家自然科学基金;

关键词：

ARIMA model; Battery swapping demand forecast; Electric bicycle; Intelligent transportation; K-means clustering;

D O I：

10.16097/j.cnki.1009-6744.2021.02.025

中图分类号：

学科分类号：

摘要：

Battery swapping cabinets have been successively distributed in cities to meet the increasing swapping demand for electric bicycles, which inevitably involves the station location of battery swapping cabinets, the sizing of battery supply, and the prediction of battery swapping demands. In order to improve the utilization rate and reduce the cost of battery swapping, a clustering and forecasting method of battery swapping demand by region is proposed. Firstly, K-means clustering was carried out on the location of the battery swapping cabinets, and the size of cabinet supply was optimized to improve the utilization rate; Then, the Autoregressive Integrated Moving Average model (ARIMA) is used to predict the short-time battery swapping demand. The results indicate that the ARIMA model has a high prediction accuracy in the demand prediction. Compared with other prediction models, better results can be achieved, which indicates that battery swapping demands tend to be linear with time. The optimization method on battery swapping cabinets and the short-term demand prediction results proposed in this paper provide data support for the location of battery swapping stations and the sizing of battery supply. Copyright © 2021 by Science Press.

引用

页码：173 / 179

页数：6

共 11 条

[1]

GB 17761-2018, E-Bike safety technical specifications, (2018)

[2]

TANG K S, HAO Z K, YI X B W, Et al., Evaluation of parking occupancy prediction methods in parking lots, Tongji University Journal (Natural Sciences Edition), 45, 4, pp. 533-543, (2017)

[3]

CHEN L, WANG J X, HE T, Et al., SVR-based regional traffic carbon emission prediction study, Journal of Transportation Systems Engineering and Information Technology, 18, 2, pp. 13-19, (2018)

[4]

ZHAO Y Y, XIA L, JIANG X G., Short-term subway passenger flow prediction model based on empirical modal decomposition and long-term memory neural networks, Journal of Traffic and Transportation Engineering, 20, 4, pp. 194-204, (2020)

[5]

LONG Y, SU Z Y, WANG Y., Monthly load forecasting based on seasonal adjustments and BP neural networks, Systems Engineering Theory and Practice, 38, 4, pp. 1052-1060, (2018)

[6]

XU D W, WANG Y D, JIA L M, Et al., Real-time road traffic state prediction based on ARIMA and Kalman filter, Frontiers of Information Technology & Electronic Engineering, 18, 2, pp. 287-303, (2017)

[7]

MILENKOVIC M, SVADLENKA L, MELICHAR V, Et al., SARIMA modelling approach for railway passenger flow forecasting, Transport, 33, 5, pp. 1113-1120, (2018)

[8]

AI X, ZHOU Z Y, WEI Y P, Et al., Bidding strategy for time-shiftable loads based on autoregressive integrated moving average model, Power System Automation, 41, 20, pp. 26-31, (2017)

[9]

SAHAI A K, RATH N, SOOD V, Et al., ARIMA modelling & forecasting of COVID-19 in top five affected countries, Diabetes and Metabolic Syndrome Clinical Research and Reviews, 14, 5, pp. 1419-1427, (2020)

[10]

BAI L., Study on the normal and unusual short-term passenger flow forecasting methods of urban rail transit, Journal of Transportation Systems Engineering and Information Technology, 17, 1, pp. 127-135, (2017)

← 1 2 →