Remote Sensing-Based Analysis of Residential Electricity Consumption Drivers in Tabriz’s Cold Semi-Arid Climate Using Machine Learning and Deep Learning

This study investigates the climatic and urban factors influencing residential electricity consumption in Tabriz, Iran, a city with a cold semi-arid climate, using remote sensing data and machine learning (Random Forest, LightGBM) and deep learning (LSTM, CNN) models. Bi-monthly data from ۲۰۰۸ to ۲۰۲۳, including LST (day/night), NDVI, NDBI, NDWI, DMSP-OLS/VIIRS Nighttime Lights, Precipitation, Relative Humidity, Solar Radiation, Sunshine Hours, and AQI (NO₂, O₃, Aerosol), were used. Electricity consumption data were obtained from the Tabriz Electricity Distribution Company. Variables were Z-score normalized and processed in Google Earth Engine. Four models—Random Forest (RF), LightGBM, LSTM, and CNN—were implemented in Google Colab. Performance was evaluated using R², RMSE, and MAE, with feature importance assessed via SHAP analysis. Random Forest outperformed others (R²=۰.۹۱, RMSE=۰.۲۹, MAE=۰.۱۷), effectively capturing nonlinear relationships. Key drivers were NDWI (۰.۲۲۸), Night Light (۰.۲۱۱), and Solar Radiation (۰.۱۸۹), highlighting water scarcity, urbanization, and radiative heating impacts. LightGBM showed moderate performance (R²=۰.۶۴), emphasizing NDBI and Precipitation, while LSTM and CNN underperformed (R²<۰) due to limited data and non-sequential variables. SHAP and residual analyses confirmed RF’s robust, stable predictions with minimal bias compared to LightGBM’s scale-dependent errors and deep models’ irregular residuals. In Tabriz’s cold semi-arid climate, residential electricity consumption is driven by NDWI, urbanization (Night Light), and solar radiation. These reflect increased heating/cooling needs due to low precipitation, urban heat islands, and rising temperatures. Green infrastructure and passive solar design are recommended to mitigate demand. This approach suits data-scarce regions, with potential enhancements via household behavioral data integration.

کلیدواژه ها:

Residential Electricity Consumption ، remote sensing ، Cold semi-arid climate ، machine learning ، Deep learning