The research focuses on one of the most demanding charging environments in Türkiye: the Bolu-Elmalık region, the busiest charging location on the Anatolian Highway that connects Istanbul and Ankara, one of the country’s most heavily trafficked intercity corridors. Seventeen charging stations in the region were analyzed, and crucially, the study did not rely on simulated or synthetic data. Real-time consumption measurements were obtained directly from the local distribution system operator, Sakarya Elektrik Dağıtım A.Ş., known as SEDAS. This grounding in operational grid data gives the findings a credibility that laboratory-scale experiments often lack, because the irregular, spiky, and highly variable consumption patterns of highway charging stations are exactly the kind of signal that defeats naive forecasting approaches.

The context for this work is a market in the midst of a genuine boom. While Türkiye’s electric vehicle market initially lagged behind Europe’s, recent years have seen a dramatic acceleration, driven by regulations issued by the Electricity Market Regulatory Authority, a rapid increase in the number of charging operators and installed charging stations, and the arrival of electric vehicles priced comparably to conventional cars. The entry of domestically produced electric vehicles into the market has further accelerated adoption. As the authors note, this surge in vehicle numbers translates directly into rapidly rising energy demand, which in turn necessitates new investments in power grids and the development of flexible, accessible grid infrastructure. Forecasting is the foundation of that planning process.

At the heart of the study lies a two-level hybrid stacking ensemble model, an architecture that combines the strengths of several different learning algorithms rather than betting on any single one. In the first stage, three primary learners make independent predictions of hourly consumption: the Extra Trees Regressor, the LightGBM Regressor, and the XGBoost Regressor. All three belong to the family of tree-based ensemble methods, which build large collections of decision trees and aggregate their outputs, but they differ in how those trees are constructed and how aggressively they correct their own errors. Extra Trees introduces additional randomness in tree splitting to reduce variance, while LightGBM and XGBoost are gradient boosting methods that build trees sequentially, with each new tree trained to fix the residual mistakes of its predecessors.

The clever part of the stacking design is what happens next. Instead of simply averaging the three base learners’ outputs, the model feeds their predictions into a second-stage meta-learner built on Ridge Regression, a regularized form of linear regression. Ridge Regression, originally introduced by Hoerl and Kennard in 1970, adds a penalty term that shrinks coefficients and guards against overfitting, which is particularly valuable when the inputs to the meta-learner, the outputs of correlated tree models, are themselves highly interrelated. By letting a simple, stable linear model learn the optimal weighting of the three powerful but heterogeneous tree models, the stacking framework captures complex nonlinear patterns in the first stage while maintaining accuracy and stability in the final prediction. This division of labor is precisely what allowed the hybrid model to significantly outperform every individual base learner on its own.

Before any modeling began, the researchers confronted a problem that plagues many machine learning applications in energy systems: multicollinearity among input variables. When predictors are strongly correlated with one another, models can become unstable and their interpretations misleading. The team assessed this using Variance Inflation Factor analysis, a standard statistical diagnostic that quantifies how much the variance of an estimated coefficient is inflated by correlation among the predictors, allowing problematic variables to be identified and handled prior to model training. This preprocessing step reflects a broader lesson for applied machine learning: careful statistical hygiene before training often matters as much as the sophistication of the algorithm itself.

Interpretability received equally serious attention. The researchers applied SHAP analysis, short for SHapley Additive exPlanations, a technique rooted in cooperative game theory that assigns each input feature a contribution value for every individual prediction. Developed by Lundberg and Lee, SHAP has become the gold standard for explaining the behavior of complex ensemble models, and here it served two purposes: identifying the most influential attributes driving the consumption forecasts and pinpointing which of the seventeen stations exerted the greatest influence on model estimates. For grid operators, this kind of transparency is not a luxury. Understanding why a model predicts a demand surge, and which stations or temporal patterns drive it, transforms a black-box forecast into an actionable planning tool.

The performance numbers tell a compelling story. In addition to the R² of 0.979, the hybrid model achieved a mean absolute error of 69.906 kilowatt-hours and a root mean square error of 100.745 kilowatt-hours in hourly consumption forecasting. In practical terms, this means the model can track the hourly load profile of a busy highway charging hub with errors small enough to be genuinely useful for operational decisions. The authors emphasize that the approach aims to achieve high accuracy and stability in time-series consumption forecasting by combining the powerful learning capabilities of tree-based heterogeneous models under a regularized linear meta-model, a formulation that balances flexibility with robustness in a way single models struggle to match.

The implications extend well beyond one highway corridor in Türkiye. Accurate forecasting of load profiles and consumption patterns at electric vehicle charging stations enables improvements across a range of critical grid functions, including optimal grid planning, demand-side management, grid flexibility, load shifting, and peak shaving. Peak shaving, in particular, is a pressing concern: uncoordinated fast charging can create sharp demand spikes that force utilities to invest in expensive peaking capacity or risk overloading local transformers. A reliable hourly forecast allows operators to anticipate those spikes, shift flexible loads, deploy storage strategically, and defer costly infrastructure upgrades. As electric vehicle adoption accelerates globally, the gap between charging demand and grid capacity will widen in many regions, and tools like this stacking ensemble offer a way to manage that transition intelligently rather than reactively.

The study was carried out within the GARDEN project, short for Grid-Aware Decarbonization of Electricity-driven Neighbourhoods, and was supported by the Scientific and Technological Research Council of Türkiye under the 1071 Programme within the Driving Urban Transitions Partnership, co-funded by the European Commission. The authors gratefully acknowledge SEDAS for providing the charging data that made the analysis possible. While data privacy considerations mean the underlying dataset cannot be shared, the methodology itself, combining multicollinearity screening, heterogeneous tree-based base learners, a regularized meta-learner, and explainability analysis, offers a replicable blueprint for distribution system operators anywhere facing the same challenge. As the electric vehicle revolution rolls onward, the grids that power it will increasingly depend on models like this one to see the demand coming before it arrives.

Subject of Research: Hybrid machine learning forecasting of regional electric vehicle charging demand from real distribution grid data

Article Title: Regional EV charging demand estimation based on real grid data using a hybrid stacking ensemble model

Article References: Regional EV charging demand estimation based on real grid data using a hybrid stacking ensemble model. (n.d.). https://doi.org/10.1007/s10586-026-06533-8

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06533-8

Keywords: EV charging demand forecasting, machine learning, stacking ensemble, XGBoost, LightGBM, Extra Trees, Ridge Regression, SHAP, power grid planning, electric vehicles, time-series forecasting, smart grids