University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 3 2026 09 23 Forecasting Residential Electricity Demand under Climate Change Scenarios: A Python-based Model Incorporating Cooling and Heating Degree Days Forecasting Residential Electricity Demand under Climate Change Scenarios: A Python-based Model Incorporating Cooling and Heating Degree Days 433 453 243406 10.22034/judpm.2026.579252.1097 FA Mona Mirrazavi M.Sc. Student of Energy Systems Engineering, Faculty of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran 0009-0007-6038-5423 Younes Noorollahi Full Professor of Energy Systems Engineering, Faculty of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran Saba Amouzadeh M.Sc. Student of Energy Systems Engineering, Faculty of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran Mohanna Baba Hoseinpour M.Sc. Student of Energy Systems Engineering, Faculty of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran Hosein Yousefi Full Professor of Energy Systems Engineering, Faculty of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran 0000-0002-6372-5127 Journal Article 2026 01 28 <span>This research aims to forecast the long-term household electricity demand under various climate change scenarios, including temperature increases of 1°C and 0.5°C per year, as well as decreases of 1°C and 0.5°C per year, utilizing CMIP6 climate projection models</span><span lang="EN">. With the rise in average temperatures and the intensification of heatwaves, cooling requirements in the residential sector have increased, leading to higher summer peak loads. To analyze this trend, a data-driven framework is proposed that employs a multivariate annual regression model incorporating Cooling Degree Days (CDD), Heating Degree Days (HDD), the number of subscribers, and temporal trends. Furthermore, daily temperature data are used to disaggregate annual consumption into daily and hourly scales, enabling the examination of peak load variations. Model evaluation indicates highly accurate performance, with coefficients of determination (R²) of 0.995 for the training set and 0.979 for the test set. The Root Mean Square Error (RMSE) is approximately 547.82 kWh, corresponding to less than one percent of annual consumption. Climate scenario analysis reveals that the CDD index is the primary driver of rising electricity use in Tehran expanding from roughly 780 units in the baseline year to 3,000 in the mild warming scenario and over 7,200 units in the severe one, representing growth of 285% to 820%. Conversely, the HDD index declines under warmer scenarios, contributing little to overall demand. Forecasts suggest that household electricity consumption will increase by 75–132% by 2050, with the difference between the warmest and coldest scenarios exceeding 77,000 kWh. Hourly analysis shows an evening peak load of approximately 21.5 kWh and an early-morning minimum of about 7 kWh. The proposed framework can serve as an effective tool for power system planning and load management under global warming conditions.</span> <span>This research aims to forecast the long-term household electricity demand under various climate change scenarios, including temperature increases of 1°C and 0.5°C per year, as well as decreases of 1°C and 0.5°C per year, utilizing CMIP6 climate projection models</span><span lang="EN">. With the rise in average temperatures and the intensification of heatwaves, cooling requirements in the residential sector have increased, leading to higher summer peak loads. To analyze this trend, a data-driven framework is proposed that employs a multivariate annual regression model incorporating Cooling Degree Days (CDD), Heating Degree Days (HDD), the number of subscribers, and temporal trends. Furthermore, daily temperature data are used to disaggregate annual consumption into daily and hourly scales, enabling the examination of peak load variations. Model evaluation indicates highly accurate performance, with coefficients of determination (R²) of 0.995 for the training set and 0.979 for the test set. The Root Mean Square Error (RMSE) is approximately 547.82 kWh, corresponding to less than one percent of annual consumption. Climate scenario analysis reveals that the CDD index is the primary driver of rising electricity use in Tehran expanding from roughly 780 units in the baseline year to 3,000 in the mild warming scenario and over 7,200 units in the severe one, representing growth of 285% to 820%. Conversely, the HDD index declines under warmer scenarios, contributing little to overall demand. Forecasts suggest that household electricity consumption will increase by 75–132% by 2050, with the difference between the warmest and coldest scenarios exceeding 77,000 kWh. Hourly analysis shows an evening peak load of approximately 21.5 kWh and an early-morning minimum of about 7 kWh. The proposed framework can serve as an effective tool for power system planning and load management under global warming conditions.</span> https://www.judpm.ir/article_243406_8267f94c8c785b3f23f8a5fc15dc3b54.pdf