Department of Computer Engineering, Jeju National University, Jeju 63243, Republic of Korea.
Department of Computer Engineering, Major of Electronic Engineering, Jeju National University, Institute of Information Science & Technology, Jeju 63243, Republic of Korea.
Sensors (Basel). 2023 Jan 4;23(2):594. doi: 10.3390/s23020594.
In terms of electric vehicles (EVs), electric kickboards are crucial elements of smart transportation networks for short-distance travel that is risk-free, economical, and environmentally friendly. Forecasting the daily demand can improve the local service provider's access to information and help them manage their short-term supply more effectively. This study developed the forecasting model using real-time data and weather information from Jeju Island, South Korea. Cluster analysis under the rental pattern of the electric kickboard is a component of the forecasting processes. We cannot achieve noticeable results at first because of the low amount of training data. We require a lot of data to produce a solid prediction result. For the sake of the subsequent experimental procedure, we created synthetic time-series data using a generative adversarial networks (GAN) approach and combined the synthetic data with the original data. The outcomes have shown how the GAN-based synthetic data generation approach has the potential to enhance prediction accuracy. We employ an ensemble model to improve prediction results that cannot be achieved using a single regressor model. It is a weighted combination of several base regression models to one meta-regressor. To anticipate the daily demand in this study, we create an ensemble model by merging three separate base machine learning algorithms, namely CatBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The effectiveness of the suggested strategies was assessed using some evaluation indicators. The forecasting outcomes demonstrate that mixing synthetic data with original data improves the robustness of daily demand forecasting and outperforms other models by generating more agreeable values for suggested assessment measures. The outcomes further show that applying ensemble techniques can reasonably increase the forecasting model's accuracy for daily electric kickboard demand.
就电动汽车 (EV) 而言，电动滑板车是短途出行、安全、经济且环保的智能交通网络的关键要素。预测日需求量可以提高本地服务提供商的信息获取能力，并帮助他们更有效地管理短期供应。本研究使用来自韩国济州岛的实时数据和天气信息开发了预测模型。电动滑板车租赁模式下的聚类分析是预测过程的一个组成部分。由于训练数据量少，我们一开始无法取得显著的结果。我们需要大量的数据才能得出可靠的预测结果。为了后续的实验步骤，我们使用生成对抗网络 (GAN) 方法创建了合成时间序列数据，并将合成数据与原始数据相结合。结果表明，基于 GAN 的合成数据生成方法具有提高预测准确性的潜力。我们采用集成模型来提高预测结果，这是单个回归模型无法实现的。它是将几个基础回归模型组合到一个元回归模型中。为了预测本研究中的日需求量，我们通过合并三个独立的基础机器学习算法，即 CatBoost、随机森林 (RF) 和极端梯度提升 (XGBoost)，创建了一个集成模型。使用一些评估指标评估了所提出策略的有效性。预测结果表明，将合成数据与原始数据混合使用可以提高日需求量预测的稳健性，并通过生成更符合建议评估指标的数值，优于其他模型。结果还表明，应用集成技术可以合理提高每日电动滑板车需求预测模型的准确性。
