Towards Sustainable Architecture: Machine Learning for Predicting Energy Use in Buildings

The ever-increasing demand for energy necessitates innovative solutions, particularly within the built environment. Buildings, often exceeding 30% of global energy consumption, present a significant opportunity for optimization. This project delves into the potential of predictive analytics to demystify building energy use and empower intelligent management practices. By harnessing the power of machine learning (ML) algorithms, the project aims to unlock hidden patterns within historical data on energy consumption, weather conditions, and building characteristics. ML's ability to identify complex relationships allows the project to uncover the intricate interplay between factors like occupancy schedules, equipment operation, and external temperature fluctuations. The project envisions a future where building managers are equipped with powerful insights. The predictive model empowers proactive management strategies, enabling the identification of peak demand periods. Armed with this knowledge, building managers can implement targeted solutions like integrating renewable energy sources. This proactive approach not only optimizes energy use but also translates to significant cost savings. Furthermore, the project champions sustainable practices. Data-driven decision-making, empowered by the predictive model, allows building managers to adopt energy conservation measures and explore renewable energy integration. This paves the way for a more sustainable future, minimizing a building's environmental impact.