This study applies a simulation- and optimization-based framework using artificial neural networks for the model predictive control (MPC) of space heating systems. The case study is a real low-energy building located in Benevento (South Italy). 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. A Pareto multi-objective approach is applied, modeling thermal comfort via the adaptive theory of ASHRAE 55, i.e., assessing a comfort penalty function. The optimization problem is solved by running a genetic algorithm, using nonlinear autoregressive networks with exogenous inputs (NARX) as simulation tool. The nets are trained on the outputs of a validated EnergyPlus model, showing good agreement. The framework is tested addressing a typical day of the winter season and using EnergyPlus weather data to simulate weather forecasts. The proposed optimal solution presents running cost for heating of 1.1 c€/m 2 day and a daily comfort penalty of 15 °C h. This means a cost saving around 9% and a reduction of discomfort around 7% compared to a reference control strategy at fixed setpoint, i.e., 21°C. Besides the proposed virtual implementation, the framework can be integrated into automation systems for real-time MPC.

Model predictive control based on genetic algorithm and neural networks to optimize heating operation of a real low-energy building / Aruta, G.; Ascione, F.; Bianco, N.; De Masi, R. F.; Mauro, G. M.; Vanoli, G. P.. - (2022), pp. 1-6. (Intervento presentato al convegno SpliTech 2022 tenutosi a Bol, Brac, Split (HR) nel 05-08 July 2022) [10.23919/SpliTech55088.2022.9854312].

Model predictive control based on genetic algorithm and neural networks to optimize heating operation of a real low-energy building

Aruta G.;Ascione F.;Bianco N.;
2022

Abstract

This study applies a simulation- and optimization-based framework using artificial neural networks for the model predictive control (MPC) of space heating systems. The case study is a real low-energy building located in Benevento (South Italy). 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. A Pareto multi-objective approach is applied, modeling thermal comfort via the adaptive theory of ASHRAE 55, i.e., assessing a comfort penalty function. The optimization problem is solved by running a genetic algorithm, using nonlinear autoregressive networks with exogenous inputs (NARX) as simulation tool. The nets are trained on the outputs of a validated EnergyPlus model, showing good agreement. The framework is tested addressing a typical day of the winter season and using EnergyPlus weather data to simulate weather forecasts. The proposed optimal solution presents running cost for heating of 1.1 c€/m 2 day and a daily comfort penalty of 15 °C h. This means a cost saving around 9% and a reduction of discomfort around 7% compared to a reference control strategy at fixed setpoint, i.e., 21°C. Besides the proposed virtual implementation, the framework can be integrated into automation systems for real-time MPC.
2022
978-1-6654-8828-0
Model predictive control based on genetic algorithm and neural networks to optimize heating operation of a real low-energy building / Aruta, G.; Ascione, F.; Bianco, N.; De Masi, R. F.; Mauro, G. M.; Vanoli, G. P.. - (2022), pp. 1-6. (Intervento presentato al convegno SpliTech 2022 tenutosi a Bol, Brac, Split (HR) nel 05-08 July 2022) [10.23919/SpliTech55088.2022.9854312].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/896798
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