Enhancing EV Charging Networks: Advanced Fusion Techniques with Insights from LSTM, Bayesian Networks, and Deep Learning
Keywords:
Electric Vehicles, Long Short-Term Memory, Data Fusion, Energy Management, ForecastingAbstract
This paper introduces a novel method that uses a multi-input LSTM model to precisely predict the charging loads of electric vehicles (EVs), which is essential for efficient energy management at charging stations. By utilizing particular characteristics such as temperature, humidity, and wind speed from the UCI database, the model analyzes this data to produce accurate forecasts. The integration of diverse inputs through the incorporation of a Bayesian network for data fusion improves the predictions given by LSTM. Comparative assessments of various input factors demonstrate differing levels of accuracy in predicting energy consumption patterns, highlighting the crucial importance of certain inputs in improving predictive performance. The study assesses the accuracy of LSTM predictions by comparing them to real energy consumption data within a 24-hour timeframe, offering useful information to enhance future forecasting techniques. This study highlights the significance of selecting suitable input variables to maximize the performance of LSTM models and their crucial role in effectively controlling energy requirements at electric vehicle charging stations.
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