I submitted a paper to the special session on simulation and optimization in building automation at the tenth annual IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014)
The paper is an extension to our previous work on uncertainty propagation from sensing to modeling and control for buildings.
The extensions are two-fold
(1) Unlike the simplified assumption in the ModelIQ paper, here we assume that uncertainty in data is not a fixed bias but is a random gaussian variable .
(2) We use non parametric statistical methods to quantify the uncertainty from real sensor data and present a method to determine the optimal placement and density of the sensors.