A Multi-Scale Approach to Exploiting Measured and Modelled Building Performance Data to Improve Campus Operations

Campuses, communities, and other building clusters are major users of energy and water and thus can have a significant environmental impact. Frequently, their buildings’ resource consumption is metered at various levels of spatial and temporal resolution to track and reduce it. However, the metering and data logging systems are often inconvenient and difficult to access due to use of multiple systems and technologies of varying vintages. Moreover, modern commercial buildings have complex mechanical systems and heat transfer paths, and these are typically difficult to visualize. Furthermore, current data availability and visualization tools do not lend themselves to identification of inefficiencies and possible solutions.This work is divided into two main parts. The aim of the first part is to provide a useful workflow and set of methods to enhance campus operations. Within the scope of this work, by using a combination of measured data and models, a comprehensive energy use assessment at different scales can be formed. This information can yield greater insights about opportunities for operational improvements and retrofits that would not be available through measurements alone. This work involves the application and testing on Carleton University campus its Canal Building to ensure that the theory is grounded in practicality and it also allows the usability to be tested on real stakeholders (building operators, campus planners, architects and accountants).The second part deals with practical elements of application and dissemination. In this part, a workflow is developed to automate the process of creating Sankey diagrams from energy simulation outputs. Moreover, this part investigates the feasibility of utilizing the visualization technique (Sankey diagrams) developed in the first part to evaluate various design variants and to enhance the decision-making process.The main contributions of this research include methodologies to: 1) convert sparse sensor and sub-meter data into estimated energy flows, 2) combine measured and modeled data to provide a detailed record of buildings and campus resource consumption at a wide range of scales, 3) convert building information models (BIM) into energy models, 4) combine hybrid evidence-based, analytical optimization, and inverse calibration methods, 5) estimate the impact of unmeasured energy flows, 6) estimate upstream environmental impacts of buildings and campuses, and 7) visualize measured and modelled data using Sankey diagrams at various scales: from building system to campus level.