Bilayer stochastic optimization model for smart energy conservation systems

Qiu, 2019, Stochastic optimized chiller operation strategy based on multi-objective optimization considering measurement uncertainty, Energy Build, 195, 149, 10.1016/j.enbuild.2019.05.006 Braun, 2007, Near-optimal control strategies for hybrid cooling plants, HVAC R Res, 13, 599, 10.1080/10789669.2007.10390974 Jin, 2007, A simplified modeling of mechanical cooling tower for control and optimization of HVAC systems, Energy Convers Manag, 48, 355, 10.1016/j.enconman.2006.07.010 Bahramnia, 2019, Modeling and controlling of temperature and humidity in building heating, ventilating, and air conditioning system using model predictive control, Energies, 12, 4805, 10.3390/en12244805 Kim, 2020, Optimal control method for HVAC systems in offices with a control algorithm based on thermal environment, Buildings, 10, 95, 10.3390/buildings10050095 Salsbury, 2005, A survey of control technologies in the building automation industry, IFAC Proc Vol, 38, 90, 10.3182/20050703-6-CZ-1902.01397 Bai, 2008, Development of an adaptive Smith predictor-based self-tuning PI controller for an HVAC system in a test room, Energy Build, 40, 2244, 10.1016/j.enbuild.2008.07.002 Phalak, 2016, Performance comparison of cascade control with conventional controls in air handling units for building pressurization Almabrok, 2018, Fast tuning of the PID controller in an HVAC system using the big bang–big crunch algorithm and FPGA technology, Algorithms, 11, 146, 10.3390/a11100146 Benedetti, 2016, Energy consumption control automation using artificial neural networks and adaptive algorithms: proposal of a new methodology and case study, Appl Energy, 165, 60, 10.1016/j.apenergy.2015.12.066 Wang, 2019, Multi-criteria comprehensive study on predictive algorithm of hourly heating energy consumption for residential buildings, Sustain Cities Soc, 49, 101623, 10.1016/j.scs.2019.101623 Huang, 2009, A robust model predictive control strategy for improving the control performance of air-conditioning systems, Energy Convers Manag, 50, 2650, 10.1016/j.enconman.2009.06.014 Hu, 2019, Analysis of energy efficiency improvement of high-tech fabrication plants, Int J Low Carbon Technol, 14, 508, 10.1093/ijlct/ctz041 Suzuki, 2000, Energy saving in semiconductor fabs by out-air handling unit performance improvement Hu, 2007, A comparative study on energy consumption for HVAC systems of high-tech FABs, Appl Therm Eng, 27, 2758, 10.1016/j.applthermaleng.2007.03.016 Kircher, 2010, Cleanroom energy efficiency strategies: modeling and simulation, Energy Build, 42, 282, 10.1016/j.enbuild.2009.09.004 Tsao, 2010, Capturing energy-saving opportunities in make-up air systems for cleanrooms of high-technology fabrication plant in subtropical climate, Energy Build, 42, 2005, 10.1016/j.enbuild.2010.06.009 Jo, 2017, Energy-saving benefits of adiabatic humidification in the air conditioning systems of semiconductor cleanrooms, Energies, 10, 1774, 10.3390/en10111774 Chang, 2016, Various energy-saving approaches to a TFT-LCD panel fab, Sustainability, 8, 907, 10.3390/su8090907 Yao, 2004, Optimal operation of a large cooling system based on an empirical model based on an empirical model, Appl Therm Eng, 24, 2303, 10.1016/j.applthermaleng.2004.03.006 Chang, 2004, A novel energy conservation method—optimal chiller loading, Elec Power Syst Res, 69, 221, 10.1016/j.epsr.2003.10.012 Huang, 2016, Amelioration of the cooling load based chiller sequencing control, Appl Energy, 168, 204, 10.1016/j.apenergy.2016.01.035 Huang, 2015, A new method for the optimal chiller sequencing control Huang, 2017, Improved cooling tower control of legacy chiller plants by optimizing the condenser water set point, Build Environ, 111, 33, 10.1016/j.buildenv.2016.10.011 Park, 2018, Development of an energy cost prediction model for a VRF heating system, Appl Therm Eng, 140, 476, 10.1016/j.applthermaleng.2018.05.068 Lucia, 2014, Handling uncertainty in economic nonlinear model predictive control: a comparative case study, J Process Control, 24, 1247, 10.1016/j.jprocont.2014.05.008 Kumar, 2020, Stochastic model predictive control for central HVAC plants, J Process Control, 90, 1, 10.1016/j.jprocont.2020.03.015 Zhuang, 2020, A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties, Appl Energy, 280, 115983, 10.1016/j.apenergy.2020.115983 Garifi, 2018, Stochastic model predictive control for demand response in a home energy management system Cho, 2020, Optimal energy retrofit plan for conservation and sustainable use of historic campus building: case of cultural property building, Appl Energy, 275, 115313, 10.1016/j.apenergy.2020.115313 Wang, 2016, Dynamic heat supply prediction using support vector regression optimized by particle swarm optimization algorithm, Math Probl Eng, 2016, 10 Mawson, 2021, Optimisation of HVAC control and manufacturing schedules for the reduction of peak energy demand in the manufacturing sector, Energy, 227, 120436, 10.1016/j.energy.2021.120436 Wang, 2011, Location and allocation decisions in a two-echelon supply chain with stochastic demand–A genetic-algorithm based solution, Expert Syst Appl, 38, 6125, 10.1016/j.eswa.2010.11.008 Wang, 2015, A revised ant algorithm for solving location–allocation problem with risky demand in a multi-echelon supply chain network, Appl Soft Comput, 32, 311, 10.1016/j.asoc.2015.03.046 Kleywegt, 2002, The sample average approximation method for stochastic discrete optimization, SIAM J Optim, 12, 479, 10.1137/S1052623499363220 Cheng, 2019, Partial sample average approximation method for chance constrained problems, Optim Lett, 13, 657, 10.1007/s11590-018-1300-8 Zhang, 2018, On the feature engineering of building energy data mining, Sustain Cities Soc, 39, 508, 10.1016/j.scs.2018.02.016 Sha, 2019, A simplified HVAC energy prediction method based on degree-day, Sustain Cities Soc, 51, 101698, 10.1016/j.scs.2019.101698 Pagliarini, 2019, vol. 204, 109512 Hu, 2020, Energy savings approaches for high-tech manufacturing factories, Case Stud Therm Eng, 17, 100569, 10.1016/j.csite.2019.100569 Gao, 2018, Model-based space temperature cascade control for constant air volume air-conditioning system, Build Environ, 145, 308, 10.1016/j.buildenv.2018.09.034 Satrio, 2019, Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm, Sustain Energy Technol Assess, 35, 48 Shang, 2002, Optimization computing based on the genetic algorithm optimization toolbox in MatLab [J], Microcomp Appl, 8, 28 Liu, 2005, The application of genetic algorithm optimization toolbox in MATLAB, J Xinjiang Univ (Nat Sci Ed), 3 Ri-bo, 2005, Application of genetic algorithm toolbox based on Matlab [J], Ordnance Ind Autom, 6, 120 Jin, 2017, An Innovative genetic algorithms-based inexact non-linear programming problem solving method, J Environ Protect, 8, 231 Taheri, 2017, Economic dispatch in a power system considering environmental pollution using a multi-objective particle swarm optimization algorithm based on the Pareto criterion and fuzzy logic, Int J Energy Environ Eng, 8, 99, 10.1007/s40095-017-0233-9 Han, 2021, Electrical and thermal performance comparison between PVT-ST and PV-ST systems, Energy, 237, 121589, 10.1016/j.energy.2021.121589 Bertsimas, 2018, Robust sample average approximation, Math Program, 171, 217, 10.1007/s10107-017-1174-z