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This dissertation presents a predictive machine learning model aimed at forecasting energy consumption trends in fully electric homes, taking into account the occupancy status of the building. It highlights the limitations of existing Building Energy Simulation (BES) tools and proposes a novel co-simulation method that integrates EnergyPlus with machine learning techniques to enhance accuracy in energy consumption predictions. The model demonstrates a significant improvement in estimation accuracy, achievin
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- d755yc94tq
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- EN