Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model

There is growing concern about how to mitigate climate change in which the reduction of CO2 emissions plays an important role. Buildings have gained attention in recent years since they are responsible for around 30% of greenhouse gases. In this context, advance control strategies to optimize HVAC systems are necessary because they can provide significant energy savings whilst maintaining indoor thermal comfort. Simulation-based model predictive control (MPC) procedures allow an increase in building energy performance through the smart control of HVAC systems. The paper presents a methodology that overcomes one of the critical issues in using detailed building energy models in MPC optimizations—computational time. Through a case study, the methodology explains how to resolve this issue. Three main novel approaches are developed: a reduction in the search space for the genetic algorithm (NSGA-II) thanks to the use of the curve of free oscillation; a reduction in convergence time based on a process of two linked stages; and, finally, a methodology to measure, in a combined way, the temporal convergence of the algorithm and the precision of the obtained solution.

[1]  Cheol-Yong Jang,et al.  Development of a model predictive control framework through real-time building energy management system data , 2015 .

[2]  Philippe Rigo,et al.  A review on simulation-based optimization methods applied to building performance analysis , 2014 .

[3]  J. Salom,et al.  Review of control strategies for improving the energy flexibility provided by heat pump systems in buildings , 2019, Journal of Process Control.

[4]  Xu Dan,et al.  Robust model predictive control for greenhouse temperature based on particle swarm optimization , 2018, Information Processing in Agriculture.

[5]  Farrokh Janabi-Sharifi,et al.  Theory and applications of HVAC control systems – A review of model predictive control (MPC) , 2014 .

[6]  Germán Ramos Ruiz,et al.  Analysis of uncertainty indices used for building envelope calibration , 2017 .

[7]  Theis Heidmann Pedersen,et al.  Multi-market demand response using economic model predictive control of space heating in residential buildings , 2017 .

[8]  Lieve Helsen,et al.  Comparison of load shifting incentives for low-energy buildings with heat pumps to attain grid flexibility benefits , 2016 .

[9]  Alberto Bemporad,et al.  Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities , 2018 .

[10]  Gerardo Maria Mauro,et al.  A new comprehensive approach for cost-optimal building design integrated with the multi-objective model predictive control of HVAC systems , 2017 .

[11]  Fariborz Haghighat,et al.  A software framework for model predictive control with GenOpt , 2010 .

[12]  Jin Woo Moon,et al.  ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms , 2015 .

[13]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[14]  Haralambos Sarimveis,et al.  A Simulated Annealing Algorithm for Prioritized Multiobjective Optimization—Implementation in an Adaptive Model Predictive Control Configuration , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Jiangang Lu,et al.  Nonlinear model predictive control based on support vector machine and genetic algorithm , 2015 .

[16]  Peter R. Armstrong,et al.  Modeling Environment for Model Predictive Control of Buildings , 2014 .

[17]  Aris Tsangrassoulis,et al.  Algorithms for optimization of building design: A review , 2014 .

[18]  Lei Chen,et al.  A new model predictive control scheme for energy and cost savings in commercial buildings: An airport terminal building case study , 2015 .

[19]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[20]  Martin Kozek,et al.  Implementation of cooperative Fuzzy model predictive control for an energy-efficient office building , 2018 .

[21]  Stefano Cordiner,et al.  Energy management in a domestic microgrid by means of model predictive controllers , 2016 .

[22]  Jonathan A. Wright,et al.  A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization , 2004 .

[23]  Paul Cooper,et al.  Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage , 2017 .

[24]  Xiwang Li,et al.  Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions , 2016 .

[25]  Germán Ramos Ruiz,et al.  Towards a new generation of building envelope calibration , 2017 .

[26]  Gerardo Maria Mauro,et al.  Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort , 2016 .

[27]  Aitor J. Garrido,et al.  Optimization of the Heating System Use in Aged Public Buildings via Model Predictive Control , 2016 .

[28]  Benjamin Paris,et al.  Heating control schemes for energy management in buildings , 2010 .

[29]  Balaji Rajagopalan,et al.  Model-predictive control of mixed-mode buildings with rule extraction , 2011 .

[30]  Yacine Rezgui,et al.  A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control , 2018 .

[31]  Germán Ramos Ruiz,et al.  Validation of calibrated energy models: Common errors , 2017 .

[32]  Gregor P. Henze,et al.  A model predictive control optimization environment for real-time commercial building application , 2013 .

[33]  Jin Wen,et al.  Review of building energy modeling for control and operation , 2014 .

[34]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[35]  Navid Delgarm,et al.  Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony (ABC) , 2016 .

[36]  William D'haeseleer,et al.  Control of heating systems in residential buildings: Current practice , 2008 .

[37]  Gregor P. Henze,et al.  Evaluating synergistic effect of optimally controlling commercial building thermal mass portfolios , 2015 .

[38]  Germán Ramos Ruiz,et al.  Genetic algorithm for building envelope calibration , 2016 .

[39]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[40]  Esmaeel Khanmirza,et al.  Design and experimental evaluation of model predictive control vs. intelligent methods for domestic heating systems , 2017 .

[41]  Arthur L. Dexter,et al.  The potential for energy saving in heating systems through improving boiler controls , 2004 .

[42]  Kaamran Raahemifar,et al.  Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system , 2017 .