LSTM-Based Forecasting and Analysis of EV Charging Demand in a Dense Urban Campus
This work addresses EV charging load forecasting for urban campus energy management, but it is incremental as it applies an existing LSTM method to new data.
The paper tackled forecasting electric vehicle (EV) charging demand using an LSTM-based framework, achieving accurate predictions across daily, weekly, and monthly time scales to aid infrastructure planning and energy management.
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.