AI-DRIVEN DECENTRALIZED HYBRID RENEWABLE ENERGY SYSTEMS FOR CLIMATE-RESILIENT URBAN DEVELOPMENT: A CASE STUDY OF KARACHI

Abstract

The increasing need for sustainable and resilient urban energy systems, due to urbanization and climate change, requires a shift from traditional central energy generation systems. In this research, a hybrid decentralized renewable energy model is introduced, incorporating photovoltaic (PV) systems, wind, energy storage, and artificial intelligence (AI) driven optimization to improve urban energy sustainability.

The research adopts a holistic systems-based approach using multi-source data, machine long short-term memory (LSTM) and multi-objective optimization to assess the performance under current and climate change-based scenarios. The city of Karachi, Pakistan, is considered as a case study owing to its high energy consumption, regular load-shedding and rich renewable energy resources.

Findings show that the proposed decentralized hybrid system enhances urban energy performance by reducing grid dependency by 40-45%, CO₂ emissions by 25-30%, and annualized energy costs by 20-25%, while boosting system reliability to more than 95% served load. The AI-based forecasting system optimises the operational efficiency of the system by incorporating accurate demand forecasting and optimal energy dispatch.

The results show the potential of hybrid renewable energy systems and smart optimization, offering a scalable, data-driven approach for sustainable urban energy transition. This research provides valuable policy insights for urban planners and policymakers to facilitate the shift towards sustainable, resilient, and decentralized energy systems in rapidly urbanizing regions.