Neural Network based HVAC Predictive Control

This paper addresses the problem of controlling Heating Ventilation and Air Conditioning (HVAC) systems with the purpose of maintaining a desired thermal comfort level, whilst minimizing the electrical energy required. Using a pilot installation, in the University of Algarve, Portugal, a Model Based Predictive Control (MBPC) strategy is used to control the HVAC equipment. The thermal comfort is assessed using the predicted mean vote (PMV) index. The MBPC methodology uses predictive models, implemented by radial basis function neural networks, identified by means of a Multi-Objective Genetic Algorithm (MOGA). Experimental results show that this approach is feasible and robust, and able to obtain energy savings greater than 50%, under normal building occupation.