A cleaner demand-side management approach utilizing hybrid fractal-fuzzy intelligence for energy loss reduction in smart energy systems

The challenge of energy loss caused by end-users' energy consumption patterns is complex, primarily due to the lack of standardized expectations among end-users and the challenges of defining demand similarity or dissimilarity in an imperfectly bounded context. At present, a fully predictive and systematic method is lacking to address these challenges. This study analyses the impact of these constraints and examines the underlying causes of energy loss resulting from end-user behaviour, which are linked to factors such as system command, control, efficiency, and cost. Managing the impact of electricity users on energy loss remains a complex challenge. Systematically classifying and regulating end-user demands comprehensively requires various societal governance measures to maintain control over grid stability. This research supports the need for such measures to optimise electricity tariffs and demand-side management. The study proposes the use of an auto-similarity intensity function to link system memory strength to demand similarity. The hybrid approach integrates classical binary logic with fuzzy logic, which acts as an adaptive learning method, to optimise energy demand systems. The study follows a two-stage analytical framework: first, a fractal geometric structure is used to explore the inductive complexity of the system's memory; second, fuzzy logic is employed to replicate human reasoning in the electricity demand sector. By processing nonlinear data with imprecision, the hybrid approach aligns the system with a structured optimization framework. The results demonstrate that while the grid energy operator defines an optimal demand range of 0.4-0.46 kWh per given operating period of half an hour, this is not equivalent to the necessary household capacity. Due to intermittent renewable sources and operational constraints, an installed capacity of 2.455 kWh per household over 30 min is necessary, especially when integrating storage batteries and PV. This ensures grid stability regardless of actual electricity demand. This paper provides critical insights into Home Area Network (HAN) systems' memory and evaluates their fuzzy logic functionalities to inform policies that reduce energy loss in the electric grid. The study demonstrates that the context-dependent relationship between the Hurst Exponent and the fuzzification of C-means, combined with predictive fuzzy modelling, effectively optimizes energy demand capacity while accommodating nonlinear system behaviour.