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

Ascione, Fabrizio; Bianco, Nicola; Stasio, Claudio De; Mauro, Gerardo Maria; Vanoli, Giuseppe Peter

doi:10.1016/j.enbuild.2015.11.033

Cited by 193 publications

(102 citation statements)

References 32 publications

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“…Maximum value of PPD and daily energy cost have been chosen as the objective function. The same function was implemented in the study by Ascione et al [35] where the HVAC hourly set point temperatures were optimized, with a day-ahead planning horizon, based on weather forecasts and occupancy profiles. Ascione et al [36] proposed a new multi-stage framework for cost-optimal analysis by multi-objective optimization and artificial neural networks, called CASA.…”

Section: Introductionmentioning

confidence: 99%

“…Such assumptions provided for good thermal comfort even in well-insulated buildings, where the passive cooling was hardly used. Some works [34][35][36] introduced thermal comfort index as an extra objective function. All the articles described above assume constant infiltration rates during simulations (occasionally selected from the set of discrete values).…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Optimization of the Envelope of Building with Natural Ventilation

Grygierek

Ferdyn-Grygierek

2018

Energies

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Abstract:A properly designed house should provide occupants with the high level of thermal comfort at low energy demand. On many occasions investors choose to add additional insulation to the buildings to reduce heat demand. This may lead to overheating of the building without a cooling system in summer periods (these prevail in Poland). Additionally, it affects the deterioration of thermal comfort, which can only be improved by increasing ventilation. The paper presents the multi-objective optimization of the selected design parameters in a single-family building in temperate climate conditions. The influence of four types of windows, their size, building orientation, insulation of external wall, roof and ground floor and infiltration on the life cycle costs and thermal comfort is analyzed for the building without cooling. Infiltration changes during the simulation and is controlled by a special controller. Its task is to imitate the behavior of occupants in changing the supply airflow. Optimal selection of the design parameters is carried out using Non-dominated Sorting Genetic Algorithm II (NSGA-II) by coupling the building performance simulation program EnergyPlus with optimization environment. For the single-family house, optimal values of design variables for three different criteria are presented.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of the Envelope of Building with Natural Ventilation

Grygierek

Ferdyn-Grygierek

2018

Energies

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“…Some researches apply multi-objective [2,5,6,25,26,[36][37][38][39][40] or multi-criterion [41,42] optimization models. In the study conducted by Ascione et al [39], a procedure that combines EnergyPlus simulation and a genetic algorithm was implemented to determine the best solutions for the HVAC system control in a residential building.…”

Section: Introductionmentioning

confidence: 99%

“…In the study conducted by Ascione et al [39], a procedure that combines EnergyPlus simulation and a genetic algorithm was implemented to determine the best solutions for the HVAC system control in a residential building. The objective of this study was to minimize the primary energy demand and investment cost.…”

Section: Introductionmentioning

confidence: 99%

Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms

Ferdyn-Grygierek

Grygierek

2017

Energies

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Abstract:The building sector is one of the largest energy consumers in the world, comprising about 40% of the total energy consumption in numerous countries. Early design decisions have a significant impact on the energy performance of buildings. The paper presents the multi-variable optimization of the selected design parameters in a single-family building in temperate climate conditions. The influence of four types of windows, their size, building orientation, insulation of external wall, roof and ground floor and infiltration on the life cycle costs (LCC) is analyzed. Optimal selection of the design parameters is carried out using genetic algorithms by coupling the building performance simulation program EnergyPlus with optimization environment. The simulations were conducted for seven optimization cases. The analysis is performed for two variants of a building with heating and cooling systems and with a heating system only. Depending on the analyzed case, the life cycle costs decreased from 7% to 34% LCC value of the reference building. In the case of temperate climate, the building optimization (in terms of heat demand only) substantially reduces the heating costs, yet the summer thermal comfort conditions deteriorate significantly.

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“…The results indicate that the MPC obtains energy savings varying between 10% and 15% with respect to simple on/off controllers. Finally, a simulation-based MPC procedure is presented in Ascione et al [12], in which the EnergyPlus software, which is a building performance simulation tool, is combined with an optimization engine, in this case MATLAB ® , to achieve a multi-objective optimization of the HVAC system's daily operation; specifically, the MPC tries to optimize the operating cost for space heating and the thermal comfort, quantified by the predicted percentage of dissatisfied (PPD). The results show that the proposed MPC is able to reduce the operating cost for space heating equal to 56%, whereas the PPD is reduced about 8%.…”

Section: Introductionmentioning

confidence: 99%

An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building

et al. 2017

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Abstract:The goal of maintaining users' thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users' productivity. This paper presents an economic model-based predictive control (MPC) whose main strength is the use of the day-ahead price (DAP) in order to predict the energy consumption associated with the heating, ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a high thermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time, the energy consumption associated with it, as much as possible. Later, the performance of the proposed control system is tested through simulations with a non-linear model of a bioclimatic building room. Several simulation scenarios are considered as a test-bed. From the obtained results, it is possible to conclude that the control system has a good behavior in several situations, i.e., it can reach the users' thermal comfort for the analyzed situations, whereas the HVAC use is adjusted through the DAP; therefore, the energy savings associated with the HVAC is increased.

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

Cited by 193 publications

References 32 publications

Multi-Objective Optimization of the Envelope of Building with Natural Ventilation

Multi-Objective Optimization of the Envelope of Building with Natural Ventilation

Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms

An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building

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