Optimization for the Model Predictive Control of Building HVAC System and Experimental Verification

Si, Qiang; Wu, Jianjun; Li, Yuan; Cai, H.

doi:10.3390/buildings12101602

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…The unknown parameters of the model (model coefficients) are estimated using measured data and parameter estimation techniques. Parameter identification of RC models can be computationally expensive, and different methods, such as model reduction, are often used to improve the computational cost [20,51]. Some examples of studies applying the gray-box modeling approach can be found in [52][53][54][55][56][57][58].…”

Section: Mpc Paradigmsmentioning

confidence: 99%

Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm

Bamdad,

Mohammadzadeh,

Cholette

et al. 2023

Buildings

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The deployment of model-predictive control (MPC) for a building’s energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm.

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Section: Mpc Paradigmsmentioning

confidence: 99%

Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm

Bamdad,

Mohammadzadeh,

Cholette

et al. 2023

Buildings

View full text Add to dashboard Cite

show abstract

“…A multitude of techniques has been devised for optimizing HVAC systems, which can be broadly classified into three categories: artificial intelligence (AI), simulation, and regression analysis. Simulation tools like DOE-2, ESP-r [10], TRNSYS [11], EnergyPlus and ACO [12] are used to estimate CL when complete building data is accessible. Accurately measuring different building parameters, however, presents practical challenges [13].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Building Energy Efficiency: A Hybrid Meta-Heuristic Approach for Cooling Load Prediction

Wang,

Zhou,

Deng

et al. 2024

IJACSA

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The research tackles the complex problem of accurately predicting cooling loads in the context of energy efficiency and building management. It presents a novel approach that increases the precision of cooling load forecasts by utilizing machine learning (ML). The main objective is to incorporate a hybridization strategy into Radial Basis Function (RBF) models, a commonly used method for cooling load prediction, to improve their effectiveness. This new method significantly increases accuracy and reliability. The resulting hybrid models, which combine two powerful optimization techniques, outperform the state-of-the-art approaches and mark a major advancement in predictive modelling. The study performs in-depth analyses to compare standalone and hybrid model configurations, guaranteeing an unbiased and thorough performance evaluation. The deliberate choice of incorporating the Self-adaptive Bonobo Optimizer (SABO) and Differential Squirrel Search Algorithm (DSSA) underscores the significance of leveraging the distinctive strengths of each optimizer. The study delves into three variations of the RBF model: RBF, RBDS, and RRBSA. Among these, the RBF model, integrating the SABO optimizer (RBSA), distinguishes itself with an impressive R 2 value of 0.995, denoting an exceptionally close alignment with the data. Furthermore, a low Root Mean Square Error (RMSE) value of 0.700 underscores the model's remarkable precision. The research showcases the effectiveness of fusing ML techniques in the RBSA model for precise cooling load predictions. This hybrid model furnishes more dependable insights for energy conservation and sustainable building operations, thereby contributing to a more environmentally conscious and sustainable future.

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“…Si et al [15] optimised the method for HVAC load prediction. The proposed prediction model was simplified and reduced order for time-efficient simulation and control, by integrating an equivalent envelope structure, heat gain calculation linearization and building pre-processing module in OpenModelica.…”

mentioning

confidence: 99%

Application of Emerging Technologies to Improve Construction Performance

Jun

Liu

et al. 2023

Buildings

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Optimization for the Model Predictive Control of Building HVAC System and Experimental Verification

Cited by 5 publications

References 27 publications

Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm

Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm

Enhancing Building Energy Efficiency: A Hybrid Meta-Heuristic Approach for Cooling Load Prediction

Application of Emerging Technologies to Improve Construction Performance

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