@article{10.11648/j.ijrse.20241304.14,
  author = {Archita Ruby Arun},
  title = {Optimizing EV Charging Infrastructure: A Data-Driven Approach to Predicting Power Demand and Analyzing Geographic Disparities
},
  journal = {International Journal of Sustainable and Green Energy},
  volume = {13},
  number = {4},
  pages = {100-108},
  doi = {10.11648/j.ijrse.20241304.14},
  url = {https://doi.org/10.11648/j.ijrse.20241304.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijrse.20241304.14},
  abstract = {This paper presents a data-driven approach to optimizing electric vehicle (EV) charging infrastructure using a stacked ensemble learning model, which predicts power demand (kWh) per session to address challenges like long wait times, geographic disparities, and uneven resource allocation. Leveraging data from 85 EV drivers across 105 charging stations, the study identifies critical factors influencing station performance, such as session duration, time of day, and regional demand. Extensive preprocessing steps, including cyclical encoding of time-related variables, one-hot encoding of categorical features, and standardization of numerical variables, ensured the dataset was properly prepared for machine learning analysis. The stacked ensemble model combines Random Forest, XGBoost, and Neural Network models, effectively capturing both linear and non-linear relationships in the data. The results highlight significant urban-rural disparities in charging infrastructure. Urban stations exhibit higher and more consistent demand, whereas rural areas show sporadic and limited usage, underscoring the need for targeted infrastructure investment in underserved regions. Temporal patterns further reveal peak charging demand during business hours at workplace stations, emphasizing the potential for dynamic optimization of station placement and operational capacity based on usage trends. The model achieved a low Mean Squared Error (MSE) on training data (0.1577 kWh), but a higher MSE on test data (1.7875 kWh) indicates overfitting, suggesting the need for further refinement. Despite this limitation, the model offers valuable insights into optimizing EV charging networks, enabling policymakers and developers to improve infrastructure planning and reduce geographic inequities. Future work will focus on expanding the dataset to include residential and public charging scenarios, incorporating additional variables like weather and traffic patterns, and refining model architecture to improve generalization. This study contributes to building equitable and efficient EV charging networks, supporting the growing adoption of sustainable transportation.
},
 year = {2024}
}

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TY  - JOUR
T1  - Optimizing EV Charging Infrastructure: A Data-Driven Approach to Predicting Power Demand and Analyzing Geographic Disparities

AU  - Archita Ruby Arun
Y1  - 2024/12/25
PY  - 2024
N1  - https://doi.org/10.11648/j.ijrse.20241304.14
DO  - 10.11648/j.ijrse.20241304.14
T2  - International Journal of Sustainable and Green Energy
JF  - International Journal of Sustainable and Green Energy
JO  - International Journal of Sustainable and Green Energy
SP  - 100
EP  - 108
PB  - Science Publishing Group
SN  - 2575-1549
UR  - https://doi.org/10.11648/j.ijrse.20241304.14
AB  - This paper presents a data-driven approach to optimizing electric vehicle (EV) charging infrastructure using a stacked ensemble learning model, which predicts power demand (kWh) per session to address challenges like long wait times, geographic disparities, and uneven resource allocation. Leveraging data from 85 EV drivers across 105 charging stations, the study identifies critical factors influencing station performance, such as session duration, time of day, and regional demand. Extensive preprocessing steps, including cyclical encoding of time-related variables, one-hot encoding of categorical features, and standardization of numerical variables, ensured the dataset was properly prepared for machine learning analysis. The stacked ensemble model combines Random Forest, XGBoost, and Neural Network models, effectively capturing both linear and non-linear relationships in the data. The results highlight significant urban-rural disparities in charging infrastructure. Urban stations exhibit higher and more consistent demand, whereas rural areas show sporadic and limited usage, underscoring the need for targeted infrastructure investment in underserved regions. Temporal patterns further reveal peak charging demand during business hours at workplace stations, emphasizing the potential for dynamic optimization of station placement and operational capacity based on usage trends. The model achieved a low Mean Squared Error (MSE) on training data (0.1577 kWh), but a higher MSE on test data (1.7875 kWh) indicates overfitting, suggesting the need for further refinement. Despite this limitation, the model offers valuable insights into optimizing EV charging networks, enabling policymakers and developers to improve infrastructure planning and reduce geographic inequities. Future work will focus on expanding the dataset to include residential and public charging scenarios, incorporating additional variables like weather and traffic patterns, and refining model architecture to improve generalization. This study contributes to building equitable and efficient EV charging networks, supporting the growing adoption of sustainable transportation.

VL  - 13
IS  - 4
ER  - 

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