Multi-Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model

Abstract

We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multiple inputs to a Long Short-Term Memory (LSTM) model to achieve a robust prediction of charging loads. Weather conditions are significant influencers of the behavior of EV drivers and their driving patterns. These behavioral and driving patterns affect the charging patterns of the drivers. Rather than one prediction (step, model, or variables) made by conventional LSTM models, three charging load (energy demand) predictions of EVs were made depending on different multi-feature inputs. Data fusion was used to combine and optimize the different charging load prediction results. The performance of the final implemented model was evaluated by the mean absolute prediction error of the forecast. The implemented model had a prediction error of 3.29%. This prediction error was lower than initial prediction results by the LSTM model. The numerical results indicate an improvement in the performance of the EV load forecast, indicating that the proposed model could be used to optimize and improve EV load forecasts for electric vehicle charging stations to meet the energy requirements of EVs.

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

“…A multi-feature data fusion technique combined with LSTM was proposed by Aduama et al [27] to improve the EV charging station load forecasting. They generate three sets of inputs for LSTM consisting of load and weather data pertaining to different historical periods.…”

“…Nevertheless, they represent a great foundation for forecasting EV charging load. On the other hand, EV-related studies [10,[24][25][26][27][28][29] do consider EV charging but they do so for a group of EVs, parking lots, charging-station, or regions, and do not confider forecasting load for individual households in presence of EVs. In contrast, we focus on predicting power consumption for individual households in presence of EV charging.…”

Research on the forecast of new energy vehicle ownership and charging load in China

“…Alizadeh et al propose a stochastic model, based on queueing theory, for electric vehicle and plug-in hybrid electric vehicle charging demand [3] . Aduama et al propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model [4] . Unterluggauer et al assesses the performance of a multivariate multi-step charging load prediction approach based on the long shortterm memory (LSTM) and commercial charging data [5] .…”

Demand Time Series Prediction of Stacked Long Short-Term Memory Electric Vehicle Charging Stations Based on Fused Attention Mechanism

“…Traditional electric load forecasting methods typically hinge on weather factors (such as temperature and humidity) [12], whereas forecasting for charging stations is more significantly influenced by user behavior and flexibility factors associated with charging [13,14]. Scholars predominantly focus on spatiotemporal load information related to electric vehicle (EV) charging [15]. Building upon this foundation, many researchers have applied artificial neural networks [16], support vector machines [17], deep learning techniques [18,19], and other methodologies to achieve precise and efficient prediction of charging loads.…”