Energy Management and Edge-Driven Trading in Fractal-Structured Microgrids: A Machine Learning Approach

Mostafa Pasandideh, Jason Kurz and Mark Apperley ()
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Mostafa Pasandideh: Ahuora—Centre for Smart Energy Systems, School of Computing and Mathematical Sciences, University of Waikato, Hamilton 3240, New Zealand
Jason Kurz: Department of Mathematics, University of Waikato, Hamilton 3240, New Zealand
Mark Apperley: Ahuora—Centre for Smart Energy Systems, School of Computing and Mathematical Sciences, University of Waikato, Hamilton 3240, New Zealand

Energies, 2025, vol. 18, issue 11, 1-19

Abstract: The 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.

Keywords: streaming machine learning (SML); Hoeffding trees; renewable energy; microgrid; battery size optimization; adaptive energy management; battery lifecycle; digital twin; grid dependence; real-time forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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