Machine learning for subnational residential electricity demand forecasting to 2050 under shared socioeconomic pathways: Comparing tree-based, neural and kernel methods

A scenario-based machine learning framework is presented for long-term, subnational electricity demand forecasting, integrating Shared Socioeconomic Pathways (SSPs) with spatially downscaled demographic, economic, and climatic variables. Using Turkey as a case study, the framework projects residential electricity demand to 2050 across all 81 provinces. The subnational approach enables the use of data-intensive machine learning algorithms by expanding the training dataset through the multiplicative effect of combining spatial and temporal dimensions. Six machine learning models: tree-based (Random Forest, XGBoost), neural networks (Feed-forward Neural Network, Long Short-Term Memory), and kernel-based methods (Support Vector Regression, Gaussian Process Regression), are systematically compared against a traditional linear regression benchmark. Random Forest achieves the highest accuracy (R2= 0.9359, MAE= 0.04 TWh), outperforming neural and kernel-based models and substantially improving on the linear baseline. Socioeconomic variables, especially family households, population, and GDP, have a greater influence on electricity demand than climatic indicators such as heating and cooling degree days. Turkey’s residential electricity demand is projected to increase by 78% from 65.5 TWh in 2023 to 116.7±2.9 TWh by 2050, with substantial variation across provinces. The spatial variation in demand forecasts highlights the value of subnational modelling for energy planning and the limitations of national-level projections. The use of SSPs enables a consistent and policy-relevant exploration of plausible long-term demand trajectories. By combining subnational resolution, scenario-based inputs, and a structured comparison of algorithm families, the study offers a transferable framework for electricity demand forecasting in regionally diverse or data-scarce contexts, supporting infrastructure planning and decarbonisation strategies.