AI for Sustainable Development: Applying Machine Learning to Optimize Energy Efficiency in Smart Cities

Abstract

A number of challenges, opportunities towards the increasing use of Artificial Intelligence (AI) for energy efficiency sector in smart cities Our work focuses on three challenges for these models: 1) the scalability and integration of AI in a wide range of urban contexts,2), long-term sustainability and assessment of the impact driven by an energy system based on AI solutions;3), ethical concern to society that is required due respect. Current research calls for resilient AI solutions capable of being tailored to each city differently based on the infrastructure and technological advancements they have invested in. As a solution, modular AI frameworks and simple models can be used for scalability and interoperability. Furthermore, because most studies consider only short-term benefits this study calls attention to the need of Life Cycle Impact Assessment (LCA) type tools for continuous monitoring and evaluation with respect long term sustainability aspects. This is further exacerbated by the larger surface area of ethical issues, such as biases and social impacts — that can be surfaced only through building rigorous ethical AI frameworks and engaging with stakeholders. In this study, a multi-methods approach involving pilot testing, scenario analysis and real-time monitoring evaluates AI-powered energy management systems. S-curves and scenario analysis graphs visualize the evolution of energy savings, CO2 emissions reductions as well as forecasting accuracy improvements. The research aims to integrate these approaches for a comprehensive view of AI in contributing sustainable energy management, particularly smart cities.