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

Prince Aduama (), Zhibo Zhang and Ameena S. Al-Sumaiti ()
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Prince Aduama: Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Shakhbout Bin Sultan St Zone 1, Abu Dhabi 127788, United Arab Emirates
Zhibo Zhang: Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Shakhbout Bin Sultan St Zone 1, Abu Dhabi 127788, United Arab Emirates
Ameena S. Al-Sumaiti: Advanced Power and Energy Center, Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates

Energies, 2023, vol. 16, issue 3, 1-14

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.

Keywords: data fusion; deep learning; electric vehicle charging stations; multi-feature; load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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