A Data-Driven Approach to Energy Management in Smart Buildings

Electrical and electronics engineering

Energy efficiency in residential and smart buildings has become a critical concern due to increasing global energy demand, rising costs, and environmental challenges such as climate change. Buildings currently account for 30–40% of global energy consumption, with HVAC systems being a major contributor. Traditional building energy management systems often rely on static schedules and rule-based automation that cannot adapt to dynamic conditions, resulting in significant energy wastage and elevated operational costs. This research proposes a data-driven framework utilizing Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) to optimize energy use. The system captures real-time environmental and operational data through IoT-enabled sensors to identify patterns and predict demand autonomously. Machine learning models, particularly regression and deep learning techniques, learn from historical data to identify hidden correlations, enabling proactive decision-making. Real-world case studies demonstrate that these predictive analytics can lead to 15-40% energy reduction and 25% cost savings across residential and commercial buildings. The framework detailed in this report includes data collection, preprocessing, feature extraction, and performance evaluation using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). By transitioning from static to adaptive intelligent control, smart buildings can maintain occupant comfort while significantly reducing their carbon footprint and operational expenses.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.