## 3 Results
### AI in the Transformation of Local Energy Systems  
Regional coverage: This sub-section includes 10 studies. The majority of cases pertain to Europe (\(n=2\); Belgium and Italy), with additional case studies identified in North America (\(n=1\), USA). Seven studies adopt a global perspective (\(n=7\)). The application of artificial intelligence in the energy sector is driving the transformation of local energy systems by enabling resource consumption optimization, emission reduction, and enhanced resilience [140]. Research highlights a wide range of solutions in which AI contributes to improving energy efficiency--at the level of individual buildings, as well as across cities, regions, and the energy sector as a whole. In [95], the use of AI and digital construction resources--such as connected devices, semantic models, and IoT--is examined for optimizing the life cycle assessment (LCA) of buildings. AI-based analysis of energy consumption, emissions, and environmental impacts enables the development of strategies for reducing energy demand and integrating renewable energy sources, thereby supporting sustainable construction and urban planning. The study in [141] focuses on enhancing energy efficiency in urban infrastructure through the use of IoT device data. It proposes a method for appliance identification by converting one-dimensional signals into two-dimensional images, achieving an accuracy of 99.68%. This approach enables a more detailed understanding of consumption behavior and the creation of a taxonomy of data fusion strategies, contributing to more sustainable energy consumption monitoring and control. The analysis of household electricity consumption data in Los Angeles [142] enables the identification of microclimatic zones that reflect the region's topography and climate. The integration of AI with socio-economic data facilitates accurate forecasting and management of energy resources, supporting sustainable urban energy policies. To ensure sustainable data collection and processing in distributed systems, the LSCEA-AIoT framework [143] has been developed, incorporating AI and machine learning methods, including Monte Carlo tree search. This architecture reduces energy consumption, extends network lifespan, and outperforms existing solutions by optimizing processes within AIoT ecosystems. In [144], machine learning and natural language processing (NLP) techniques are applied to calculate China's Energy Digitalization Index. By integrating spatial econometric models and investment analysis in the digital industry, the study assesses the impact of digital technologies on carbon productivity, identifies key drivers of low-carbon transformation, and formulates recommendations relevant for China's local ecosystems as well developing countries. The study in [145] presents a regional support system for photovoltaic energy distribution in Emilia-Romagna. The integration of big data, climate models, and AI algorithms enables accurate forecasting of energy production and storage. Such approaches enhance energy resilience, support climate policy goals, and promote the expansion of renewable energy sources. The development of local energy markets (LEM) using AI is described in [146]. Multi-layer neural networks and Gradient Boosting models are employed to forecast energy consumption and optimize the use of battery energy storage systems (BESS), thereby strengthening the resilience of local energy ecosystems and reducing risks for market participants. In [147], examples are provided of micro grids managed by AI technologies to achieve Sustainable Development Goal 7 (SDG7). AI technologies improve the efficiency of managing distributed generation systems across various regions, including developing areas. This not only increases the share of renewable energy but also strengthens social capital, expands access to energy, and fosters trust in emerging technological solutions. In [148], GIS and AI are used to assess the potential of non-wood biomass in Flanders and to optimize biogas supply chains.