Enhanced EV Spatial Distribution Estimation for Grid Planning using Neural Architecture Search-Guided Physics-Informed Neural Network and Pufferfish Optimization Algorithm
Electric vehicles (EV) play a vital role in modern transportation, and accurately estimating their spatial distribution is essential for effective grid planning. The utility grid management system will optimize energy utilization while balancing network capacity and develop new infrastructure compon...
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| Vydáno v: | 2025 7th International Conference on Inventive Material Science and Applications (ICIMA) s. 460 - 466 |
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IEEE
28.05.2025
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| Abstract | Electric vehicles (EV) play a vital role in modern transportation, and accurately estimating their spatial distribution is essential for effective grid planning. The utility grid management system will optimize energy utilization while balancing network capacity and develop new infrastructure components which reduce the electrical grid stress during EV integration increases. However, estimating EV distribution in grid planning is challenging due to unpredictable charging patterns, dynamic mobility behavior, and varying energy demands, which complicate load forecasting and infrastructure optimization. To overcome these drawbacks, this paper proposes a hybrid approach for spatial distribution of EV. The process begins by gathering data from vehicle registration dataset, which is then passed through a pre-processing phase. Regularized Bias-Aware Ensemble Kalman Filter (RBAEKF) is employed to clean and remove the missing value in the input data. The pre-processed output was fed to Neural Architecture Search-Guided Physics-Informed Neural Network (NASPINN) the data enters the classification phase, to enhance the accuracy of classifications. The class 0, class 1 and class 2 of EV distribution is successfully classified by using NASPINN. The weight parameter of NASPINN is optimized using Pufferfish Optimization Algorithm (POA). The NASPINN-POA technique is implemented in MATLAB and evaluated using various performance metrics, including accuracy, precision, recall, F1-score, specificity and Root Mean Squared Error (RMSE). The results show that the NASPINN-POA method outperforms existing approaches, such as Sparrow Search Algorithm-Back Propagation Neural Network (SSA-BPNN), Particle Swarm Optimization (PSO), Eurasian Oystercatcher Optimizer-Quantum Neural Network (EOO-QNN), Bayesian Network (BN) and Long Short-Term Memory (LSTM). The proposed NASPINN-POA method enables an accurate spatial distribution of EV with 98.8% accuracy, 98.4% recall and achieves an MAE of 1.07 to optimize grid planning by minimizing errors. |
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| AbstractList | Electric vehicles (EV) play a vital role in modern transportation, and accurately estimating their spatial distribution is essential for effective grid planning. The utility grid management system will optimize energy utilization while balancing network capacity and develop new infrastructure components which reduce the electrical grid stress during EV integration increases. However, estimating EV distribution in grid planning is challenging due to unpredictable charging patterns, dynamic mobility behavior, and varying energy demands, which complicate load forecasting and infrastructure optimization. To overcome these drawbacks, this paper proposes a hybrid approach for spatial distribution of EV. The process begins by gathering data from vehicle registration dataset, which is then passed through a pre-processing phase. Regularized Bias-Aware Ensemble Kalman Filter (RBAEKF) is employed to clean and remove the missing value in the input data. The pre-processed output was fed to Neural Architecture Search-Guided Physics-Informed Neural Network (NASPINN) the data enters the classification phase, to enhance the accuracy of classifications. The class 0, class 1 and class 2 of EV distribution is successfully classified by using NASPINN. The weight parameter of NASPINN is optimized using Pufferfish Optimization Algorithm (POA). The NASPINN-POA technique is implemented in MATLAB and evaluated using various performance metrics, including accuracy, precision, recall, F1-score, specificity and Root Mean Squared Error (RMSE). The results show that the NASPINN-POA method outperforms existing approaches, such as Sparrow Search Algorithm-Back Propagation Neural Network (SSA-BPNN), Particle Swarm Optimization (PSO), Eurasian Oystercatcher Optimizer-Quantum Neural Network (EOO-QNN), Bayesian Network (BN) and Long Short-Term Memory (LSTM). The proposed NASPINN-POA method enables an accurate spatial distribution of EV with 98.8% accuracy, 98.4% recall and achieves an MAE of 1.07 to optimize grid planning by minimizing errors. |
| Author | Chadge, Rajkumar Al Jawad, Osama Jamal Jamil Abed Sivaramkrishnan, M. S, Yashashwini M, Arun Ramkumar, M. Siva |
| Author_xml | – sequence: 1 givenname: M. Siva surname: Ramkumar fullname: Ramkumar, M. Siva email: sivaram0699@gmail.com organization: SNS College of Technology,Dept of ECE,India – sequence: 2 givenname: M. surname: Sivaramkrishnan fullname: Sivaramkrishnan, M. email: krishbe95@gmail.com organization: Karpagam College of Engnieering,Dept of EEE,India – sequence: 3 givenname: Arun surname: M fullname: M, Arun email: arunkarthik116@gmail.com organization: Saveetha University,Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS),Department of Mechanical Engineering,Chennai,India,602105 – sequence: 4 givenname: Yashashwini surname: S fullname: S, Yashashwini email: yashaswini.cse@cambridge.edu.in organization: Cambridge Institute of Technology,Department of CSE,Bengaluru,India,560086 – sequence: 5 givenname: Osama Jamal Jamil Abed surname: Al Jawad fullname: Al Jawad, Osama Jamal Jamil Abed email: ojawad@zu.edu.jo organization: Zarqa University,Faculty of Economic and Administrative Sciences, BIT,Zarqa,Jordan – sequence: 6 givenname: Rajkumar surname: Chadge fullname: Chadge, Rajkumar email: rbchadge@rediffmail.com organization: Yeshwantrao Chavan College of Engineering,Nagpur,India |
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| Snippet | Electric vehicles (EV) play a vital role in modern transportation, and accurately estimating their spatial distribution is essential for effective grid... |
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| SubjectTerms | Accuracy Classification algorithms Distribution functions Electric Vehicle Estimation Graphical models Kalman filters Long short term memory Neural Architecture Search-Guided Physics-Informed Neural Network Neural networks Optimization Planning Pufferfish Optimization Algorithm and Regularized Bias-Aware Ensemble Kalman Filter |
| Title | Enhanced EV Spatial Distribution Estimation for Grid Planning using Neural Architecture Search-Guided Physics-Informed Neural Network and Pufferfish Optimization Algorithm |
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