Model-based predictive maintenance in building automation systems with user discomfort

This work presents a new methodology for quantifying the discomfort caused by non-optimal temperature regulation, in a building automation system, as a result of degraded biomass boiler operation. This discomfort is incorporated in a model-based dynamic programming algorithm that computes the optimal maintenance action for cleaning or replacing the boiler. A non-linear cleaning model is used to represent the different cleaning strategies under taken by contractors. The maintenance strategy minimizes the total operational costs of the boiler, the cleaning costs and the newly defined discomfort costs, over a long-term prediction horizon that captures the short-term daily thermal comfort within the heating zone. The approach has been developed based on real data obtained from a biomass boiler at a Spanish school and the resulting optimal maintenance strategies are shown to have the potential of significant energy and cost savings.