The work described in this paper relates to advanced control systems, specifically designed for heating, ventilation and air conditioning in office buildings. This work specifically focuses on the use of state of the art fan coil units with advanced instrumentation and control. The premise of the work is that control systems can be significantly enhanced by using real-time data from a distributed sensor network deployed in the building. Specifically, the performance of control systems can be improved by augmenting predictive (feed-forward) control operations with techniques to improve the accuracy of models. A control algorithm for heating, ventilating and air-conditioning systems is described in this paper that integrates an advanced feedforward control algorithm with conventional feedback control. This paper further contains a description of a functional prototype used to demonstrate the proposed control algorithm for indoor thermal environmental control. The test-bed used in this work — the Robert L Preger Intelligent Workplace (IW), at Carnegie Mellon University, involves a large number of variables and hence a complex control task, i.e., the test bed contains multiple sources of thermal energy, and multiple constraints and disturbances — both measurable and immeasurable. The algorithms demonstrated in this test-bed are expected to perform satisfactorily on other environments with smaller number of variables. This paper contains a description of experiments that were performed to validate the comfort and energy benefits of increased sensing using fan coil units that are in installed in two spaces in the IW.
Methodology of software development for artificial microclimate control systems
