IMPROVING URBAN ENERGY RESILIENCE WITH AN INTEGRATIVE FRAMEWORK BASED ON MACHINE LEARNING METHODS

Abstract

Introduction: Climate change and global warming are among the greatest challenges facing the world today. A new concept, known as urban resilience, has been developed in response. There are various approaches to urban resilience. Among them, is the urban energy resilience (UER) approach, which poses a considerable challenge. Machine learning (ML), as an application of artificial intelligence (AI), provides powerful and affordable computing resources, large-scale data mining, advanced algorithms, and real-time monitoring. However, very few studies have investigated how such aspects can be integrated into urban resilience in general, and UER in particular. Purpose of the study: The study develops an integrative framework that can improve UER, based on ML methods. Methodology: We carried out a bibliometric analysis and a systematic review of UER in accordance with AI concepts, models, and applications. Results: The findings of this study were used to create an integrative framework, based on three hierarchical phases, which effectively addressed the main capabilities of UER, identified its priorities, and shed light on how ML can benefit UER as a whole. Novelty: The framework developed in this study also offers insights in integrating ML methods into UER as strategically as possible, especially in the context of climate change and urban energy systems. This framework can serve as reference for specialists and decision-makers aiming to expand AI and ML applications to optimize UER.