The use of artificial intelligence (AI) and machine learning (ML) models to forecast urban energy consumption is becoming more widespread, but these models often rely on large, clean and well-distributed datasets. In reality, particularly at a local level, the available data are often limited, inconsistent or incomplete. This study systematically examines the robustness and reliability of AI predictive models in urban small data conditions using a real energy dataset from a neighborhood monitored over 24 months. The analysis compares several ML models trained on progressively shorter historical windows (6, 12, 18 and 24 months) and assesses performance degradation through controlled data quality stress tests, including missing values and noise. Results show that ensemble-based models achieve high accuracy when at least 18–24 months of data are available (normalized R2 up to 0.87), while performance declines markedly below 12 months. Gradient boosting demonstrates the highest robustness under severe data constraints, maintaining normalized R2 values above 0.70 with 12 months of data. Regularized linear models perform competitively in longer, well-structured time series but degrade under extreme data scarcity. An ultra-conservative data augmentation strategy yields limited but consistent improvements (≈1–2%) in short-horizon scenarios.
Piras et al. (Sat,) studied this question.