使用混合深度学习方法进行时间序列预测：一种Bi-LSTM嵌入去噪自编码器Transformer
Time Series Forecasting Using a Hybrid Deep Learning Method: A Bi-LSTM Embedding Denoising Auto Encoder Transformer
AI总结：
本文提出Bi-LSTM嵌入去噪自编码器模型BDM用于短期电动汽车充电负荷预测，在五个时间步中的四个上优于Transformer、CNN、RNN、LSTM和GRU等基准模型，提升了时间序列预测与决策能力。
摘要：
时间序列数据是各个领域中普遍存在的一种数据形式，由随时间采集的一系列测量值组成。预测是时间序列模型的一项关键应用，即基于历史数据预测未来值。准确的预测对于各行业做出明智决策至关重要。就电动汽车（EV）而言，精确预测在基础设施发展规划、负载均衡和能源管理中发挥着关键作用。本研究提出了一种Bi-LSTM嵌入去噪自编码器模型（BDM），旨在解决时间序列问题，重点关注短期电动汽车充电负荷预测。通过将所提模型与Transformer、CNN、RNN、LSTM和GRU等基准模型进行比较来评估其性能。研究结果表明，所提模型在五个时间步中的四个上优于基准模型，证明了其在时间序列预测中的有效性。本研究对增强时间序列预测能力做出了重要贡献，从而改进了决策过程。
英文摘要：
Time series data is a prevalent form of data found in various fields. It consists of a series of measurements taken over time. Forecasting is a crucial application of time series models, where future values are predicted based on historical data. Accurate forecasting is essential for making well-informed decisions across industries. When it comes to electric vehicles (EVs), precise predictions play a key role in planning infrastructure development, load balancing, and energy management. This study introduces a BI-LSTM embedding denoising autoencoder model (BDM) designed to address time series problems, focusing on short-term EV charging load prediction. The performance of the proposed model is evaluated by comparing it with benchmark models like Transformer, CNN, RNN, LSTM, and GRU. Based on the results of the study, the proposed model outperforms the benchmark models in four of the five-time steps, demonstrating its effectiveness for time series forecasting. This research makes a significant contribution to enhancing time series forecasting, thereby improving decision-making processes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
