Proper energy schedules are an important part of power systems planning and requires that optimizations and reliable energy forecast be made in response of demand and power economy. This paper focuses on the predictive optimization of energy to minimize the inherent costs while supplying the required energy for efficient power system operation. In this regard, the combination of adaptive hybrid leveraging on machine intelligence is used to boost a metaphor-to-metaphorless optimizer called PSO-Rao (Particle Swarm-Rao Optimizer) for solving the energy schedule/delivery problem. Simulation results show that the proposed adaptive hybrid solution can serve as a useful consensus estimator for adequate power systems planning interventions.
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