VR digital twin of office space with computer vision-based estimation of room occupancy and power consumption

In the past years, energy consumption has increased rapidly due to many factors, including the rise in technology adoption. This has many downfalls, from higher costs to CO \(_2\) ${}_2$ emissions. Human activities in offices and houses represent a considerable amount of energy usage. A digital twin (DT) of an open-plan common space is created, serving the purpose of remote room occupancy monitoring and automatic detection of energy consumption. A virtual reality (VR) model is developed and integrated to temperature, humidity and imaging sensors. For maintaining privacy, images are processed in local computers to measure occupancy levels and live video feed were never transmitted. The same set of imaging sensors were also used in a bespoke computer vision module for energy consumption estimation. The human avatars were mapped with high correlation (R \(^{2}\) ${}^2$ \(= 0.85\) $=0.85$ ) with actual positions on floor. Our energy consumption algorithm accuracy obtained true positive rate of \(91.58\%\) $91.58\%$ and F1 score of \(81.96\%\) $81.96\%$ . Finally, all this information is transmitted and visualized to the 3D digital twin for remote monitoring and simulation.