Energy submetering at the equipment level provides a tool to control energy consumption and improve equipment energy use. Virtual meters (VMs) are a cost-effective alternative to physical meters that can capture unmetered energy flows at the equipment level and provide building stakeholders with details that support their operational decisions. The virtual metered energy can be presented through interactive visualizations that allow users to interact with the graphical representations based on their operational needs to gain insights that would facilitate decision-making processes. This thesis aims to develop a suite of virtual metering algorithms to characterize unmeasured energy flows across critical heating, ventilation, and air conditioning components and present the virtual metered energy through effective visualizations that allow user interaction to gain insights into building operational decisions. To this end, the thesis structure consists of three main parts that develop equipment-level virtual meters and a standalone visualization tool to visualize the virtual metered energy. In this first part, an integrated inverse greybox AHU model is developed using data collected from a highly instrumented AHU serving an academic building in Ottawa, Canada. Optimal values of model parameters are used to create VMs that estimate the heat supplied by the heating coil, the heat extracted by the cooling coil, and the heat gains due to the supply fan. In the second part, VMs that estimate the heat added by zone-level perimeter radiant heaters are developed using steady-state, transient, and load disaggregation inverse modelling approaches. The models are trained using data collected from 18 zones in an academic building in Ottawa, Canada. The accuracy of the VMs is assessed by comparing the heat estimated by the VMs to measurements obtained from physical meters installed in the 18 zones. The modelling approaches' performance is evaluated by comparing model inputs, data processing, and the accuracy of the VMs. The third part utilizes the virtual metering algorithms presented in the first two parts in developing a standalone interactive visualization tool that illustrates equipment-level virtual metered energy trends. The potential of the visualization tool for assisting decision-making processes and improving building energy performance is demonstrated through illustrative examples.