Deep Learning-Enabled Holistic Control and Prediction System for Building Energy Consumption and Distribution Optimization

Managing a building’s energy consumption while minimizing costs and grid reliance is complex. A novel system is proposed, blending advanced technologies and methodologies. Deep learning techniques, particularly the fusion of Hough Transform within a Gated Recurrent Unit, form the core of this approach. User-defined preferences are prioritized, offering personalized energy-saving strategies. Simulation and experimentation demonstrate the system’s efficacy, forecasting energy demand and supply with 4%–10% deviation accuracy in hourly data for months ahead. By leveraging deep learning predictions, efficient energy storage, and an optimized scheduling algorithm, the system achieves an 84% reduction in grid energy reliance, cutting power expenses by 87%. Comparative cost analysis over thirty years highlights the system’s cost-effectiveness and the inefficiency of installing wind turbines on the building’s roof. In essence, this system offers a data-driven solution that optimizes energy management, reduces grid dependency, and tailors energy-saving strategies, significantly cutting electricity expenses.

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