NONE 20260512095714607 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 1991-8763 2224-2856 Stylianos Pappas Department of Electrical Engineering and Computer Science Hellenic Naval Academy Terma Chatzikyriakou, 18539, Piraeus GREECE Alexandros Gazis Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, 67100, GREECE Nikos Mastorakis Department of Electrical Engineering and Computer Science Hellenic Naval Academy Terma Chatzikyriakou, 18539, Piraeus GREECE Reliable operation of modern power systems demands accurate forecasting. Errors can reduce resilience, especially in mission-critical military, defense, and strategic infrastructures. While hybrid forecasting models perform well under normal conditions, their robustness to noisy data still needs thorough evaluation. In this work, a sensitivity and robustness analysis of a novel adaptive hybrid forecasting framework is examined using two scenarios of correlated Gaussian noise. These scenarios were selected to represent measurement degradation, communication failures, and physical disturbances commonly encountered in resource-limited defense operational environments. The proposed hybrid method integrates the Multi Model Partitioning Filter, Nonlinear Autoregressive Exogenous models, and a Genetic Algorithm for Resource Allocation, with performance evaluated using real commercial data. 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