نوع مقاله : مقاله پژوهشی

نویسندگان

• محمد نعمتی
• ایرج تیموری
• شهریور روستایی

Department of urban and regional planning, Faculty of planning and environmental sciences, University of Tabriz, Tabriz, Iran.

چکیده

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 2008 to 2023, 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=0.91, RMSE=0.29, MAE=0.17), effectively capturing nonlinear relationships. Key drivers were NDWI (0.228), Night Light (0.211), and Solar Radiation (0.189), highlighting water scarcity, urbanization, and radiative heating impacts. LightGBM showed moderate performance (R²=0.64), emphasizing NDBI and Precipitation, while LSTM and CNN underperformed (R²<0) 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.

تازه های تحقیق

This study demonstrates that machine learning models, particularly RandomForest, leveraging remote sensing data, provide robust tools for predicting domestic electricity consumption in the cold, semi-arid climate of Tabriz. The superior performance of RandomForest (R²=0.91) compared to LightGBM, LSTM, and CNN underscores its capability to model nonlinear relationships among climatic variables such as NDWI, Night Light intensity, and solar radiation. These findings align with global studies projecting up to a 55% increase in cooling demand in vulnerable regions (Auffhammer et al., 2011) and highlight the critical influence of hydrological and urbanization factors on energy consumption in arid climates (Xia et al., 2021; Santamouris et al., 2015). Satellite-derived indices like NDVI, NDWI, and LST enable precise identification of urban heat island effects and moisture deficits, which are exacerbated in Tabriz by reduced precipitation and rising land surface temperatures, posing challenges to energy infrastructure (Abulibdeh et al., 2024; Zhao et al., 2024). This approach is particularly vital for data-scarce developing countries like Iran, facilitating the development of sustainable energy policies. Future research should focus on integrating remote sensing data with household behavioral data to enhance prediction accuracy and bolster climate-resilient energy management strategies (Mastrucci et al., 2021; Diffenbaugh et al., 2007).

کلیدواژه‌ها

• Residential Electricity Consumption
• Remote Sensing
• Cold Semi-Arid Climate
• Machine Learning
• Deep Learning

موضوعات

• جغرافیا و برنامه ریزی شهری

عنوان مقاله [English]

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

نویسندگان [English]

• Mohammad Nemati
• Iraj Teimouri
• Shahrivar Roustaie

Department of urban and regional planning, Faculty of planning and environmental sciences, University of Tabriz, Tabriz, Iran.

چکیده [English]

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 2008 to 2023, 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=0.91, RMSE=0.29, MAE=0.17), effectively capturing nonlinear relationships. Key drivers were NDWI (0.228), Night Light (0.211), and Solar Radiation (0.189), highlighting water scarcity, urbanization, and radiative heating impacts. LightGBM showed moderate performance (R²=0.64), emphasizing NDBI and Precipitation, while LSTM and CNN underperformed (R²<0) 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.

کلیدواژه‌ها [English]

• Residential Electricity Consumption
• Remote Sensing
• Cold Semi-Arid Climate
• Machine Learning
• Deep Learning