GREEN ENERGY INFRASTRUCTURE IN RIYADH: ARTIFICIAL INTELLIGENCE-DRIVEN OPTIMIZATION OF RENEWABLE ENERGY SYSTEMS FOR URBAN ENVIRONMENTAL SUSTAINABILITY UNDER VISION 2030

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Saudi Arabia burns roughly 600,000 barrels of crude oil daily just to keep the lights on and the air conditioning running. In a country sitting atop the world’s second-largest proven petroleum reserves, this is not an energy crisis in the conventional sense it is an opportunity cost crisis. Every barrel burned domestically is a barrel not exported at international market prices. Riyadh, consuming approximately 22 percent of the Kingdom’s total electricity output, sits at the center of this paradox. This paper investigates the deployment of artificial intelligence systems across Riyadh’s emerging green energy infrastructure encompassing the 2.6 GW Sudair Solar PV Plant, distributed rooftop photovoltaic networks, green hydrogen pilot facilities, smart grid modernization, and district cooling optimization examining how algorithmic intelligence transforms renewable energy from an intermittent supplement into a reliable backbone for urban sustainability. Through field investigation at seven operational green energy installations, analysis of eighteen months of AI-driven grid management data from Saudi Electricity Company systems, and semi-structured interviews with fifty-one professionals across energy engineering, data science, urban planning, and regulatory governance, we demonstrate that AI-optimized renewable integration achieves 23–31 percent improvement in solar yield forecasting accuracy, 19 percent reduction in grid balancing costs, and 41 percent improvement in demand-response participation rates compared to conventional management approaches. We propose an Adaptive Energy Intelligence Architecture that integrates generation forecasting, demand prediction, storage optimization, and carbon accounting into a unified system targeting Riyadh’s transition to 50 percent renewable electricity by 2030.

Links and sources

Comments