Smart Energy Efficiency: Energy Management System in Smart Buildings based on Voting Ensemble and battery bank

Energy management in smart buildings requires strategies to improve power consumption, ensuring reliability and cost reduction. This thesis addresses the solution of the problem by implementing a predictive model based on an artificial intelligence technique working in conjunction with a proposed battery bank in order to better store energy and mitigate the high peaks of electricity demand in different periods as well as in atypical scenarios. The research addresses two aspects: accuracy in consumption prediction, and energy management, the latter including energy storage efficiency and mitigating high peak power consumption. The proposal aims for the expected results to reduce electricity consumption by 3% to 7%, validating the viability of this scalable and sustainable solution to improve energy efficiency in smart infrastructures.