(untitled) (مجلة الفاروق للعلوم, 2026)
High-voltage networks in western Libya operate under a demanding combination of pronounced daily and seasonal load variation, incomplete station-level visibility, and narrow security margins whenever a transformer or feeder is unavailable. The engineering challenge is therefore broader than forecasting accuracy. A statistically acceptable forecast can still lead to overload, voltage stress, uneven feeder loading, or emergency shedding when it is not translated into a feasible operating action. The same principle applies to photovoltaic generation (PV) can reduce daytime imports, yet an uncoordinated plant may leave the evening peak almost unchanged and can simply relocate stress within the network for these reasons, we conducted a study of a mini-loop represented by the Al-Ruwais loop in the western Libya network. This research is considered an extension of the doctoral dissertation. introduces the Hybrid Neural Forecasting Resilience Oriented Load Management (HNF-ROLM) framework for Libya’s Al-Ruwais network, using (GECOL) demand data. It integrates an (FNN) forecast with a decision algorithm coordinating (PV, BESS, DR), and reinforcement under normal and (N-1) constraints. Performance is evaluated via (ENS, SAIDI, SAIFI), and a Resilience Index (RI), moving beyond mere forecasting accuracy MATLAB software was used. Results show (PV) cuts daytime imports, while (BESS) and (DR) provide critical evening-peak and contingency support, their coordinated use significantly reduces unserved energy, outage duration/frequency, and network violations while improving (RI). The planning layer identifies when demand growth requires structural reinforcement confirming that operational flexibility and firm upgrades are complementary. HNF-ROLM strengthens resilience via PV, BESS, and DR, delivering peak relief while guiding staged reinforcement planning for future security.