The space conditioning of buildings is liable for more than 10 % of World final energy uses and related CO2-eq emissions. Such share must be drastically reduced to pursue sustainability by optimizing both energy design and devices control. In this frame, space cooling is assuming an increasing weight owing to climate change. Accordingly, this study applies a simulation- and optimization-based framework for the model predictive control (MPC) of space cooling systems. The case study is a nearly zero energy building located in Benevento – Southern Italy, Mediterranean climate – featuring an efficient air-source multi-split system for cooling. The framework is envisioned to provide optimal values of setpoint temperatures on a day-ahead planning horizon to minimize energy cost and thermal discomfort, based on weather forecasts. Accordingly, a Pareto multi-objective approach is applied considering different discomfort indicators to compare the Fanger theory with the adaptive one of ASHRAE 55. The optimization problem is solved by running a genetic algorithm – variant of NSGA II – under MATLAB® environment. The objective functions are assessed via the coupling between MATLAB® and EnergyPlus, using a validated building energy model. The multi-criteria decision-making is performed by setting a limit to discomfort to pick an optimal Pareto solution. The framework is tested addressing a typical day of the cooling season and using monitored weather data to simulate weather forecasts. Different optimal solutions are provided to fit different comfort categories. Compared to a reference control at fixed setpoint – 26 °C – the proposed solutions with similar comfort performance ensure cost savings around 28 %. Besides the proposed hypothetical implementation, the framework can be integrated in automation systems for real-time MPC. The novel contributions of this study lie in the methodology to combine MPC with different thermal comfort models as well as in the results, which provide deeps insights about the application of MPC for the space cooling of nearly zero energy buildings in a balanced climate.

Optimizing space cooling of a nearly zero energy building via model predictive control: Energy cost vs comfort

De Masi R. F.;Festa V.;Mauro G. M.
;
Vanoli G. P.
2023-01-01

Abstract

The space conditioning of buildings is liable for more than 10 % of World final energy uses and related CO2-eq emissions. Such share must be drastically reduced to pursue sustainability by optimizing both energy design and devices control. In this frame, space cooling is assuming an increasing weight owing to climate change. Accordingly, this study applies a simulation- and optimization-based framework for the model predictive control (MPC) of space cooling systems. The case study is a nearly zero energy building located in Benevento – Southern Italy, Mediterranean climate – featuring an efficient air-source multi-split system for cooling. The framework is envisioned to provide optimal values of setpoint temperatures on a day-ahead planning horizon to minimize energy cost and thermal discomfort, based on weather forecasts. Accordingly, a Pareto multi-objective approach is applied considering different discomfort indicators to compare the Fanger theory with the adaptive one of ASHRAE 55. The optimization problem is solved by running a genetic algorithm – variant of NSGA II – under MATLAB® environment. The objective functions are assessed via the coupling between MATLAB® and EnergyPlus, using a validated building energy model. The multi-criteria decision-making is performed by setting a limit to discomfort to pick an optimal Pareto solution. The framework is tested addressing a typical day of the cooling season and using monitored weather data to simulate weather forecasts. Different optimal solutions are provided to fit different comfort categories. Compared to a reference control at fixed setpoint – 26 °C – the proposed solutions with similar comfort performance ensure cost savings around 28 %. Besides the proposed hypothetical implementation, the framework can be integrated in automation systems for real-time MPC. The novel contributions of this study lie in the methodology to combine MPC with different thermal comfort models as well as in the results, which provide deeps insights about the application of MPC for the space cooling of nearly zero energy buildings in a balanced climate.
2023
HVAC systems; Model predictive control; Multi-objective genetic algorithm; Space cooling; Thermal comfort; Zero energy buildings
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/56407
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