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Open AccessArticle
Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability
by
Mohammad Aldossary
Mohammad Aldossary 1,*
,
Hatem A. Alharbi
Hatem A. Alharbi 2
and
Nasir Ayub
Nasir Ayub 3
1
Department of Computer Engineering and Information, College of Engineering, Prince Sattam bin Abdulaziz University, Wadi Al-Dawasir 11991, Saudi Arabia
2
Department of Computer Engineering, College of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi Arabia
3
Department of Creative Technologies, Air University Islamabad, Islamabad 44000, Pakistan
*
Author to whom correspondence should be addressed.
Mathematics 2024, 12(17), 2627; https://doi.org/10.3390/math12172627 (registering DOI)
Submission received: 9 July 2024
/
Revised: 16 August 2024
/
Accepted: 22 August 2024
/
Published: 24 August 2024
Abstract
As electric vehicles (EVs) are becoming more common and the need for sustainable energy practices is growing, better management of EV charging station loads is a necessity. The simple act of folding renewable power from solar or wind in an EV charging system presents a huge opportunity to make them even greener as well as improve grid resiliency. This paper proposes an innovative EV charging station energy consumption forecasting approach by incorporating integrated renewable energy data. The optimization is achieved through the application of SARLDNet, which enhances predictive accuracy and reduces forecast errors, thereby allowing for more efficient energy allocation and load management in EV charging stations. The technique leverages comprehensive solar and wind energy statistics alongside detailed EV charging station utilization data collected over 3.5 years from various locations across California. To ensure data integrity, missing data were meticulously addressed, and data quality was enhanced. The Boruta approach was employed for feature selection, identifying critical predictors, and improving the dataset through feature engineering to elucidate energy consumption trends. Empirical mode decomposition (EMD) signal decomposition extracts intrinsic mode functions, revealing temporal patterns and significantly boosting forecasting accuracy. This study introduces a novel stem-auxiliary-reduction-LSTM-dense network (SARLDNet) architecture tailored for robust regression analysis. This architecture combines regularization, dense output layers, LSTM-based temporal context learning, dimensionality reduction, and early feature extraction to mitigate overfitting. The performance of SARLDNet is benchmarked against established models including LSTM, XGBoost, and ARIMA, demonstrating superior accuracy with a mean absolute percentage error (MAPE) of 7.2%, Root Mean Square Error (RMSE) of 22.3 kWh, and R² Score of 0.87. This validation of SARLDNet’s potential for real-world applications, with its enhanced predictive accuracy and reduced error rates across various EV charging stations, is a reason for optimism in the field of renewable energy and EV infrastructure planning. This study also emphasizes the role of cloud infrastructure in enabling real-time forecasting and decision support. By facilitating scalable and efficient data processing, the insights generated support informed energy management and infrastructure planning decisions under dynamic conditions, empowering the audience to adopt sustainable energy practices.
Share and Cite
MDPI and ACS Style
Aldossary, M.; Alharbi, H.A.; Ayub, N.
Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability. Mathematics 2024, 12, 2627.
https://doi.org/10.3390/math12172627
AMA Style
Aldossary M, Alharbi HA, Ayub N.
Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability. Mathematics. 2024; 12(17):2627.
https://doi.org/10.3390/math12172627
Chicago/Turabian Style
Aldossary, Mohammad, Hatem A. Alharbi, and Nasir Ayub.
2024. "Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability" Mathematics 12, no. 17: 2627.
https://doi.org/10.3390/math12172627
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