The present work is aimed at exploring the potentials of extracting control rules of a smart glazing from an optimal control strategy obtained by means of an ideal model predictive controller (MPC). To this sake an ideal deterministic MPC (Model Predictive Control with ideal prediction of disturbances), minimising total energy use, is devised for the control of a smart glazing for a South-oriented enclosed office space in London. Secondly, a data mining-based method is adopted to extract sets of rules from the simulated MPC-controlled data set. Finally, these rules are compared with the ideal MPC performance, and with reference threshold-based control rules.

Advanced Control Strategies For The Modulation of Solar Radiation In Buildings: MPC-enhanced Rule-based Control / Piscitelli, MARCO SAVINO; Brandi, Silvio; Gennaro, Giovanni; Capozzoli, Alfonso; Favoino, Fabio; Serra, Valentina. - STAMPA. - 16:(2020). (Intervento presentato al convegno International Building Performance Simulation Conference tenutosi a Roma nel September 2019).

Advanced Control Strategies For The Modulation of Solar Radiation In Buildings: MPC-enhanced Rule-based Control

Marco Savino Piscitelli;Silvio Brandi;Giovanni Gennaro;Alfonso Capozzoli;Fabio Favoino;Valentina Serra
2020

Abstract

The present work is aimed at exploring the potentials of extracting control rules of a smart glazing from an optimal control strategy obtained by means of an ideal model predictive controller (MPC). To this sake an ideal deterministic MPC (Model Predictive Control with ideal prediction of disturbances), minimising total energy use, is devised for the control of a smart glazing for a South-oriented enclosed office space in London. Secondly, a data mining-based method is adopted to extract sets of rules from the simulated MPC-controlled data set. Finally, these rules are compared with the ideal MPC performance, and with reference threshold-based control rules.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2786416