Efficient HVAC devices are not sufficient to achieve high levels of building energy performance, since the regulation/control strategy plays a fundamental role. This study proposes a simulation-based model predictive control (MPC) procedure, consisting of the multi-objective optimization of operating cost for space conditioning and thermal comfort. The procedure combines EnergyPlus and MATLAB®, in which a genetic algorithm is implemented. The aim is to optimize the hourly set point temperatures with a day-ahead planning horizon, based on forecasts of weather conditions and occupancy profiles. The outcome is the Pareto front, and thus the set of non-dominated solutions, among which the user can choose according to his comfort needs and economic constraints. The critical issue of huge computational time, typical of simulation-based MPC, is overcome by adopting a reliable minimum run period. The procedure can be integrated in building automation systems for achieving a real-time optimized MPC. The methodology is applied to a multi-zone residential building located in the Italian city of Naples, considering a typical day of the heating season. Compared to a standard control strategy, the proposed MPC generates a reduction of operating cost up to 56%, as well as an improvement of thermal comfort.

Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort / Ascione, Fabrizio; Bianco, Nicola; De Stasio, Claudio; Mauro, GERARDO MARIA; Vanoli, Giuseppe Peter. - In: ENERGY AND BUILDINGS. - ISSN 0378-7788. - 111:(2016), pp. 131-144. [10.1016/j.enbuild.2015.11.033]

Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort

ASCIONE, FABRIZIO;BIANCO, NICOLA;
2016

Abstract

Efficient HVAC devices are not sufficient to achieve high levels of building energy performance, since the regulation/control strategy plays a fundamental role. This study proposes a simulation-based model predictive control (MPC) procedure, consisting of the multi-objective optimization of operating cost for space conditioning and thermal comfort. The procedure combines EnergyPlus and MATLAB®, in which a genetic algorithm is implemented. The aim is to optimize the hourly set point temperatures with a day-ahead planning horizon, based on forecasts of weather conditions and occupancy profiles. The outcome is the Pareto front, and thus the set of non-dominated solutions, among which the user can choose according to his comfort needs and economic constraints. The critical issue of huge computational time, typical of simulation-based MPC, is overcome by adopting a reliable minimum run period. The procedure can be integrated in building automation systems for achieving a real-time optimized MPC. The methodology is applied to a multi-zone residential building located in the Italian city of Naples, considering a typical day of the heating season. Compared to a standard control strategy, the proposed MPC generates a reduction of operating cost up to 56%, as well as an improvement of thermal comfort.
2016
Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort / Ascione, Fabrizio; Bianco, Nicola; De Stasio, Claudio; Mauro, GERARDO MARIA; Vanoli, Giuseppe Peter. - In: ENERGY AND BUILDINGS. - ISSN 0378-7788. - 111:(2016), pp. 131-144. [10.1016/j.enbuild.2015.11.033]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/635628
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 179
  • ???jsp.display-item.citation.isi??? 152
social impact