• Integrated deep learning with LEAP for long-term forecasting. • Developed building–PV scenarios for Iran’s energy system. • Incorporated policymakers’ input into MCDA weighting. • Prioritized scenarios based on Iran’s policy criteria. • Identified gaps with Paris Agreement targets. To achieve energy sustainability, Iran needs renewable energy expansion and improved demand-side energy efficiency. This study forecasts Iran’s residential electricity demand through 2044 and evaluates the combined impact of rooftop photovoltaic (PV) systems and eco-efficient natural zeolite bricks (NZB). Deep learning models trained on historical data (1991–2021) for GDP, population, and electricity consumption were used to project long-term demand. An energy-system framework then quantified grid losses, fuel use, and CO 2 emissions under alternative policy scenarios. To enhance real-world relevance, multi-criteria decision analysis (MCDA) weights were informed by Iranian energy policymakers’ perspectives. Scenarios were ranked under two perspectives: feasibility–cost (national weighting) and emissions reduction (Paris-style weighting). Under the national weighting, combining NZB with 1% rooftop PV penetration achieved the highest score (0.756), reflecting an emphasis on practicality and cost-effectiveness. In contrast, the Paris-style weighting favored combining NZB with 3% rooftop PV penetration (0.84), indicating stronger prioritization of decarbonization. These results support a phased pathway that begins with achievable domestic actions and progressively advances toward more ambitious emission reduction targets.
Azimizadeh et al. (2026) studied this question.