Abstract
The ‘ideal’ optimal control of a radiant floor heating system (RFHS) can be achieved when a central controller is fully aware of the interrelationship between the different thermal dynamics of a boiler, radiant floor, and room. This study proposed integrated control strategies that can consider the interaction of heterogeneous RFHSs using a minimalistic approach with minimal data easily available from residential buildings. Three individual artificial neural networks were developed to choreograph the heterogeneous dynamics of the RFHSs for each household. Then, model predictive control was applied to realize the real-time integrated control of the RFHSs in the residential buildings. Approximately 14–46% of heating energy could be saved by integrated control. Moreover, the potential energy saving rate from integrated control varied depending on the degree of human intervention.
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