Building Assessment Framework from Whole Building to Components
A Danish Case Study
DOI:
https://doi.org/10.34641/clima.2022.317Keywords:
Key Performance Indicators (KPIs), building systems, building assessment, energy use, rotary heat exchanger, air handling unit (AHU), open historical dataAbstract
Well-functioning buildings are crucial for the occupant's health and comfort and for reducing the CO2 emissions from the building sector. A first step in assessing a well-functioning building is to know the current state of the building by, for example, relevant Key Performance Indicators (KPIs). Choosing suitable KPIs to provide a clear message can be challenging; however, beneficial to convey a message to the building actors. This study proposes a Building Assessment Framework to mitigate the latter, consisting of 1) a flexible and novel KPI tool and 2) a step-bystep KPI assessment methodology applicable to all buildings, systems, subsystems, and components. The KPI tool provides the user with a list of KPIs suited for all building systems, and with a separate backend and frontend, it is an easy tool to use. The KPI assessment methodology will guide the user through 5 steps and propose visualization of the chosen KPIs. The step-bystep KPI assessment methodology consists of 5 steps: 1) identification of the selected building resolution level 2) selection of the KPIs for the resolution level 3 + 4) recognition and crossreferencing of necessary sensors 5) choice of benchmarking for the data. The results from the KPI assessment using historical data from a university building located in Denmark demonstrate that the KPI tool is generic, making it applicable to all levels of a building and its systems. The Building
Assessment Framework is flexible; it can be used over short and long periods (instantaneous to several years) and implemented in the building management system. However, it is necessary to be used with historical data, allowing for the real-time performance evaluation of the selected buildings or systems, thereby enabling the users to spot potential abnormal behavior that can lead to faults in the systems.
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Copyright (c) 2022 Kamilla Heimar Andersen, Simon Pommerencke Melgaard, Rasmus Lund Jensen, Thomas Fehr, Anna Marszal-Pomianowska, Per Kvols Heiselberg
This work is licensed under a Creative Commons Attribution 4.0 International License.