Modeling Urban Electricity Demand and Spatial Fairness Using Machine Learning: Evidence from New York City

Understanding electricity consumption at a fine-grained spatial level is vital for equitable infrastructure planning in cities. This study analyzes electricity usage patterns across New York City using multiple Machine Learning models, including time series forecasting with Prophet, classification with Random Forest, and regression with ensemble models. This study examine 20212024 monthly electricity consumption data at the borough and neighborhood level to identify high-demand zones, assess prediction accuracy, and evaluate spatial disparities in energy allocation. Using a combination of Gini coefficients, model residuals, and geospatial visualization, the study reveals significant inequalities in model performance and projected load trends. These findings underscore the importance of integrating fairness diagnostics into urban energy modeling, even when using standard public datasets and minimal input features.