Abstract
Ensemble Stacking Generalization has emerged as a viable method for forecasting Electric Vehicle (EV) charging behaviour. This method uses a variety of machine learning methods, such as Decision Trees (DT), Random Forests (RF), and k-nearest Neighbours (KNN), to improve predictions about charging behaviour, focusing on stay duration and energy consumption. These forecasts are based on previous charge data, and the methodology considerably improves predicted accuracy while reducing model variation, overcoming the drawbacks of single-regressor models. A thorough investigation of ACN data was used to properly collect the dataset relevant to Electric Vehicle (EV) energy usage and session length. The crucial details of EV charging behaviour were painstakingly documented in this dataset, including session length and kWh provided. A wide range of statistical evaluation measures were utilized to assess the suggested approaches' effectiveness thoroughly. The outcomes of our efforts to anticipate Electric Vehicles (EVs) energy consumption and session length highlight the superiority of Ensemble Stacking Generalization. This method regularly outperformed competing models by producing results that met the standards established by chosen evaluation metrics. The significance of this is that it emphasizes how the concepts of stacking approaches may be used to increase the accuracy of EV energy usage predictions considerably. It's also critical to note that forecasts significantly improve over earlier studies that used the same dataset. The ability of Ensemble Stacking Generalization to tackle the complexities of EV charging behaviour prediction from an original aspect is highlighted by this as being both inventive and robust.
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Availability of data and materials
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Abbreviations
- R 2 :
-
R2 Squared
- ACN:
-
Adaptive Charging Network
- API:
-
Application Programming Interface
- BEV:
-
Battery Electric Vehicle
- CART:
-
Classification and Regression Tree
- CEL:
-
Conventional Electricity Load
- DKDE:
-
Diffusion-Based Kernel Density Estimator
- DSM:
-
Demand-Side Management
- DT:
-
Decision Tree
- EPA:
-
Ensemble Predicting Algorithm
- ESG:
-
Ensemble Stack Generalization
- EV:
-
Electric Vehicle
- FCV:
-
Forward Cross-Validation
- GP:
-
Gaussian Process Regression
- HTTPS:
-
Hypertext send Protocol Secure
- HK-FmCV:
-
Hierarchical K-FmCV
- K-FmCV:
-
K-Fold m-step Cross-Validation
- K-CV:
-
K-Fold Cross-Validation
- KNN:
-
K Nearest Neighbor
- kWh:
-
Kilowatt-hours
- MLP:
-
Multilayer Perceptron
- MERRA-2:
-
Modern-Era Retrospective analysis for Research and Applications, Version 2
- MPSF:
-
Modified Pattern-Based Sequence Forecasting
- MSE:
-
Mean Squared Error
- OCPP:
-
Open Charge Point Protocol
- PCA:
-
Principal Component Analysis
- PEV:
-
Plug-in Electric Vehicle
- PII:
-
Personally Identifiable Information
- RF:
-
Random Forest
- RMSE:
-
Root Mean Squared Error
- SMAPE:
-
Symmetric Mean Absolute Percentage Error
- SOC:
-
State of Charge
- SVR:
-
Support Vector Regression
- TWDP-NN:
-
Time Weighted Dot Product Nearest Neighbour
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Conceptualization, B Anil Kumar; methodology, B Jyothi; software, Arvind R. Singh and Mohit Bajaj validation, B Anil Kumar; formal analysis, B Jyothi and Arvind R. Singh investigation, B Anil Kumar and B Jyothi; resources, B Jyothi; data curation, B Anil Kumar and B Anil Kumar writing—original draft preparation, B Anil Kumar; writing—review and editing, B Anil Kumar; visualization B Anil Kumar; supervision, B Jyothi; project administration, Mohit Bajaj. All authors have read and agreed to the published version of the manuscript.
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Kumar, B.A., Jyothi, B., Singh, A.R. et al. Enhancing EV charging predictions: a comprehensive analysis using K-nearest neighbours and ensemble stack generalization. Multiscale and Multidiscip. Model. Exp. and Des. (2024). https://doi.org/10.1007/s41939-024-00457-9
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DOI: https://doi.org/10.1007/s41939-024-00457-9