用混合模型提升电动车充电负荷短期预测准确率
Time Series Forecasting Using a Hybrid Deep Learning Method: A Bi-LSTM Embedding Denoising Auto Encoder Transformer
- 融合Bi-LSTM与去噪自编码器的嵌入结构增强时序特征提取
- 在五个时间步中四个优于主流模型，最高误差降低12.3%
- 适合电力调度、充电桩规划等需要精准负荷预测的场景
时序数据广泛存在于多个领域，其预测是关键应用之一。在电动汽车领域，精确预测对基础设施规划、负载均衡和能源管理至关重要。本文提出一种基于Bi-LSTM嵌入的去噪自编码器模型（BDM），用于短时电动车充电负荷预测。通过与Transformer、CNN、RNN、LSTM、GRU等基准模型对比，所提模型在五个预测时间步中的四个表现更优，验证了其在时序预测任务中的有效性。该研究为提升时序预测精度提供了新思路，有助于优化跨行业决策流程。
原文摘要 · Abstract (English)
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.
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