Challenges and Solutions in Managing a Real-Time Database of Monitored Buildings

Real databases that store building detection data pose significant challenges, including handling real-time data generated by remote sensing systems and effectively analyzing large volumes of Internet of Things (IoT) sensor data. Proposed solutions involve scalable databases and detection information models, while ensuring data quality and addressing measurement errors are crucial considerations. This paper explores the management of a real database containing building detection data, focusing on real-time data acquired from remote sensing systems and IoT sensors in smart buildings. The study investigates the application of advanced data management techniques and detection information models to address scalability, fault tolerance, and consistency issues in the database. It highlights the importance of utilizing local energy consumption and PV generation data for effective energy management strategies. The research presents methodologies that incorporates easily applicable algorithms and heuristics to evaluate and manipulate real datasets from monitored buildings, enabling accurate day-ahead predictions. Additionally, machine learning algorithms are employed for forecasting purposes. Through a compelling test case, the effectiveness of the proposed methodology is demonstrated, showcasing its potential to overcome database challenges and provide valuable insights for smarter building control systems and efficient energy management.