Pasandideh, MostafaKurz, Jason A.Jason A. , Mark2026-06-192026-06-192025Pasandideh, M., Kurz, J., & Apperley, M. (2025). Energy management and edge-driven trading in fractal-structured microgrids: A machine learning approach. Energies, 18(11). https://doi.org/10.3390/en181129761996-1073https://hdl.handle.net/10289/18392The integration of renewable energy into residential microgrids presents significant challenges due to solar generation intermittency and variability in household electricity demand. Traditional forecasting methods, reliant on historical data, fail to adapt effectively in dynamic scenarios, leading to inefficient energy management. This paper introduces a novel adaptive energy management framework that integrates streaming machine learning (SML) with a hierarchical fractal microgrid architecture to deliver precise real-time electricity demand forecasts for a residential community. Leveraging incremental learning capabilities, the proposed model continuously updates, achieving robust predictive performance with mean absolute errors (MAE) across individual households and the community of less than 10% of typical hourly consumption values. Three battery-sizing scenarios are analytically evaluated: centralised battery, uniformly distributed batteries, and a hybrid model of uniformly distributed batteries plus an optimised central battery. Predictive adaptive management significantly reduced cumulative grid usage compared to traditional methods, with a 20% reduction in energy deficit events, and optimised battery cycling frequency extending battery lifecycle. Furthermore, the adaptive framework conceptually aligns with digital twin methodologies, facilitating real-time operational adjustments. The findings provide critical insights into sustainable, decentralised microgrid management, emphasising improved operational efficiency, enhanced battery longevity, reduced grid dependence, and robust renewable energy utilisation.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/adaptive energy managementbattery lifecyclebattery size optimizationcomputer sciencedata analyticsdigital twingrid dependenceHoeffding treesmicrogridreal-time forecastingrenewable energysoftware engineeringstreaming machine learning (SML)Journal Article10.3390/en181129761996-107340 Engineering4008 Electrical Engineering33 Built environment and design40 Engineering51 Physical sciences7 Affordable and Clean Energy13 Climate Action