NONE 20260515082523244 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON POWER SYSTEMS 1790-5060 2224-350X Stylianos Pappas Department of Electrical Engineering and Computer Science Hellenic Naval Academy Terma Chatzikyriakou, 18539, Piraeus GREECE Nikos Mastorakis Department of Electrical Engineering and Computer Science Hellenic Naval Academy Terma Chatzikyriakou, 18539, Piraeus GREECE Accurate and computationally efficient forecasting is essential for the undisturbed operation of power systems. Traditional methods cannot detect the nonlinear behaviour of modern electrical grids, affected by various factors such as seasonal variations, peak-hour demand fluctuations the increasing penetration of renewable energy sources, etc. To overcome these limitations, an adaptive hybrid method is proposed, combining the Multi Model Partitioning Filter (MMPF) with Nonlinear Autoregressive Exogenous (NARX) models and a Genetic Algorithm for Resource Allocation (GARA). The MMPF structure remains unchanged, implementing NARX submodels to detect the non-linear data characteristics. Additionally, the GARA optimizes the MMPF overall weights through an evolutionary, procedure. The new method is evaluated using real data from the Hellenic grid and compared against two established methods, MMPF–SVM and MMPF–GA. 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