BIM-Based DSS for HVAC Root-Cause Detection

This chapter of the book examines the development and application of an algorithm model within a BIM framework to effectively diagnose and manage HVAC system issues. HVAC systems are essential as they are one of the primary energy consumers in buildings, and their malfunction can significantly affect both energy efficiency and occupant comfort. By integrating this model into BIM, facility management teams are provided with advanced tools to swiftly identify and address HVAC problems, ultimately enhancing the building's operational phase. The chapter commences with an overview of the process for developing a sophisticated algorithm model tailored to analyze the root causes of HVAC issues. This model incorporates various data inputs, including system performance data, environmental conditions, and historical maintenance records, to pinpoint potential failures or inefficiencies within the HVAC system. The ultimate objective is to identify the specific components or processes contributing to excessive energy use or system failure. The algorithm employs a systematic approach to deconstruct the operations of HVAC systems, taking into account factors such as component sizing, system integration, and operational protocols. The next section of the chapter discusses integrating the model into the BIM system. This integration enables the FM team to visualize real-time data on HVAC operations across the building's digital twin, providing a more intuitive and interactive approach to maintenance management. The BIM implementation entails aligning the algorithm's outputs with the corresponding physical components in the 3D model. This visualization enables the FM team to swiftly pinpoint any equipment malfunctions and comprehend their impact on building operations. One of the key advantages of integrating the algorithm model into BIM is the significant reduction in time required to detect and diagnose HVAC problems. With the algorithm model, the FM team can promptly identify issues and prioritize maintenance tasks based on their severity and their impact on energy consumption and occupant comfort. The chapter concludes with a case study illustrating the practical application of the algorithm model in a university building. This real-world example demonstrates how the model has successfully identified and resolved HVAC issues, leading to improved energy efficiency and occupant comfort.