用LSTM预测城市校园电动车充电需求，支持多时间尺度预测。
LSTM-Based Forecasting and Analysis of EV Charging Demand in a Dense Urban Campus
- 基于LSTM建模，融合数据归一化与插值处理原始充电数据。
- 在日、周、月三个时间尺度上实现高精度充电负荷预测。
- 模块化设计适合不同使用场景的电动车充电设施部署。
本文提出一种基于循环神经网络（特别是长短期记忆网络，LSTM）的框架，用于处理多源电动车充电负荷数据并进行未来负荷预测。该框架对来自多个地点的大量原始数据进行预处理，通过插值填补缺失值并进行归一化，再输入LSTM模型以捕捉短时波动和长期趋势。实验结果表明，该模型可在日、周、月等多时间尺度上实现精准预测，为基础设施规划、能源管理及电网接入提供重要支持。系统采用模块化设计，可适应不同充电地点的差异化使用模式，适用于多种部署场景。
原文摘要 · Abstract (English)
This paper presents a framework for processing EV charging load data in order to forecast future load predictions using a Recurrent Neural Network, specifically an LSTM. The framework processes a large set of raw data from multiple locations and transforms it with normalization and feature extraction to train the LSTM. The pre-processing stage corrects for missing or incomplete values by interpolating and normalizing the measurements. This information is then fed into a Long Short-Term Memory Model designed to capture the short-term fluctuations while also interpreting the long-term trends in the charging data. Experimental results demonstrate the model's ability to accurately predict charging demand across multiple time scales (daily, weekly, and monthly), providing valuable insights for infrastructure planning, energy management, and grid integration of EV charging facilities. The system's modular design allows for adaptation to different charging locations with varying usage patterns, making it applicable across diverse deployment scenarios.
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