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The current paper is a proposal of uncertainty-sensitive scenario-based linear programming model predictive control (LP-MPC) framework of Internet of Things (IoT) -enabled photovoltaic-battery energy storage system (PV-BESS) hybrid energy management as used in remote campus applications. The suggested system combines renewable energy production, battery energy storage, grid communication, load demand on the campuses and real-time monitoring into a unified feedback system of energy management. The energy scheduling problem is also modeled in such a way as a linear programming model to optimize the total cost of operation and meet the primary technical requirements of the hybrid system. The objective comprises grid energy cost, battery degradation cost, PV curtailment penalty, and peak- demand penalty, while the constraints are power balance, battery state of charge (SOC) limits, charging and discharging limits, PV utilization limits, and grid import capacity. The LP model is integrated into a rolling-horizon MPC approach with recent IoT measurements and short-term predictions to enhance the adaptability to operating in uncertain conditions. Various scenarios are taken to reflect an uncertainty in renewable generation and campus demand such as normal operation, low PV generation, high load demand and worst-case conditions. The simulation outcomes during a 24-hour time frame demonstrate that the suggested LP-MPC framework will minimize grid power imports, enhance PV usage, cut the operating expense, restrict peak grid-side demand, and ensure the safe operation of BESS. The findings indicate the potential of the suggested solution to adaptive energy management of remote campus hybrid energy systems.