Intelligent and Optimal Control of Air Conditioning Systems by Achieving Comfort and Minimize Energy
الموضوعات :
Yazdan Daneshvar
1
(Department of civil engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran.)
Majid Sabzehparvar
2
(Department of industrial engineering collage of engineering, karaj branch, Islamic Azad University, Karaj. Iran.)
Seyed Amir Hossein Hashemi
3
(Department of civil engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran.)
الکلمات المفتاحية: Genetic Algorithm, Artificial Intelligence, HVAC control systems, Extended Kalman-filter, artificial neural networks,
ملخص المقالة :
In this study, artificial neural networks, artificial neural network combination with genetic algorithm and neural network combination with Kalman filter were used to optimally model and control a real air conditioning system. Using the above methods, the system is first trained and after verifying the modeling accuracy, the capability of this modeling to predict the future conditions of the system is investigated. In addition to the subsystems investigated in both heating and cooling phases by mass and energy equations in Simulink simulated by Matlab software, the results of this section are finally compared with the optimal modeling results. The most important advantage of artificial neural network modeling over mass and energy equation modeling approaches is that it captures all the uncertainties and nonlinear properties of the air conditioning system due to the use of real data for modeling. It takes. Therefore, this method can optimize energy consumption in air conditioners by predicting the future conditions of the system and by precisely adjusting the time of turning on and off the main energy consuming equipment. The most important achievement of this research is more accurate and realistic modeling of the nonlinear air conditioning system.Comparing the methods used in the research for simulation methods using mass and energy equations, modeling using Bayesian trained neural network, artificial neural network modeling using MLP, modeling using neural network and genetic algorithm, modeling Using neural network and Kalman filter, the square error is equal to 0.006, 0.18, 0.056, 0.1456 and more than 0.5, respectively.
[1] Deng, Z. and Q. Chen, Development and validation of a smart HVAC control system for multi-occupant offices by using occupants’ physiological signals from wristband. Energy and Buildings, 2020: p. 109872.
[2] Aguilera, J.J., O.B. Kazanci, and J. Toftum, Thermal adaptation in occupant-driven HVAC control. Journal of Building Engineering, 2019. 25: p. 100846.
[3] Harkouss F, Fardoun F, Biwole P-H “Multi-objective optimization methodology for net-zeroenergy buildings,” Journal of Building Engineering, 2018, 16 57–71.
[4] Lorena Tuballa M, Lochinvar Abundo M, “A review of the development of Smart Grid technologies,” Renewable and Sustainable Energy Reviews, 2016 Volume 59, Pages 710-725.
[5] Mogles N, Padget J, Gabe-Thomas E, Walker I, Lee J, “computational model for designing energy behavior change interventions,” User Model User-Adap Inter, 2018, 28, 1–34.[6] Ning, M. and M. Zaheeruddin, Neuro-optimal operation of a variable air volume HVAC&R system. Applied Thermal Engineering, 2010. 30(5): p. 385-399.
[6] Mohammadi M, Noorollahi Y, Mohammadi B, Hosseinzadeh, M, Yousefi H, Torabzadeh Khorasani S, “Optimal management of energy hubs and smart energy hubs – A review,” Renewable and Sustainable Energy Reviews, 2018.
[7] Afroz, Z., et al., Modeling techniques used in building HVAC control systems: A review. Renewable and sustainable energy reviews, 2018. 83: p. 64-84.
[8] Ward, J., J. Wall, and G. Platt, HVAC control system and method. 2016, Google Patents.
[9] Wang, Y., et al., Evaluation on classroom thermal comfort and energy performance of passive school building by optimizing HVAC control systems. Building and Environment, 2015. 89: p. 86-106.
[10] Ning, M. and M. Zaheeruddin, Neuro-optimal operation of a variable air volume HVAC&R system. Applied Thermal Engineering, 2010. 30(5): p. 385-399.
[11] Xian-Mei, Z., L. Hao-Yan, and Z. Jin. GA in Optimized Control of Central Air-conditioning System Based on ANN Simulation. in Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007).
[12] Labus, J., et al. ANN application to modelling and control of small absorption chillers. in Proceedings of the BSO12-Building Simulation and Optimization Conference. 2012.
[13] Tashtoush, B., M. Molhim, and M. Al-Rousan, Dynamic model of an HVAC system for control analysis. Energy, 2005. 30(10): p. 1729-1745.
[14] Karadağ, R. and Ö. Akgöbek, The prediction of convective heat transfer in floor-heating systems by artificial neural networks. International Communications in Heat and Mass Transfer, 2008. 35(3): p. 312-325.
[15] Shen, C., L. Wang, and Q. Li, Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. Journal of materials processing technology, 2007. 183(2-3): p. 412-418.
[16] Liu, W., et al., Springback prediction for sheet metal forming based on GA-ANN technology. Journal of Materials Processing Technology, 2007. 187: p. 227-231.
[17] Huang, H., et al., A new zone temperature predictive modeling for energy saving in buildings. Procedia Engineering, 2012. 49: p. 142-151.
[18] Sum, J., et al., On the Kalman filtering method in neural network training and pruning. IEEE Transactions on Neural Networks, 1999. 10(1): p. 161-166.
[19] Wang, S. and X. Jin, Model-based optimal control of VAV air-conditioning system using genetic algorithm. Building and Environment, 2000. 35(6): p. 471-487.
[20] Lu, L., et al., HVAC system optimization—in-building section. Energy and Buildings, 2005. 37(1): p. 11-22.
[21] Maasoumy, M., Modeling and optimal control algorithm design for hvac systems in energy efficient buildings. 2014.
[22] Nakahara, N., et al. Load prediction for optimal thermal storage-comparison of three kinds of model application. in Building Simulation. 1999.
[23] Parvaresh, A., S.M.A. Mohammadi, and A. Parvaresh, A new mathematical dynamic model for HVAC system components based on Matlab/Simulink. International Journal of Innovative Technology and Exploring Engineering, 2012. 1(2): p. 1-6.
[24] Macek, K. and K. Mařík, A methodology for quantitative comparison of control solutions and its application to HVAC (heating, ventilation and air conditioning) systems. Energy, 2012. 44(1): p. 117-125.
[25] Tiğrek, T., S. Dasgupta, and T.F. Smith, Nonlinear optimal control of HVAC systems. IFAC Proceedings Volumes, 2002. 35(1): p. 149-154.
[26] Platt, G., et al., Adaptive HVAC zone modeling for sustainable buildings. Energy and Buildings, 2010. 42(4): p. 412-421.
[27] Singh, G., M. Zaheer-Uddin, and R. Patel, Adaptive control of multivariable thermal processes in HVAC systems. Energy Conversion and Management, 2000. 41(15): p. 1671-1685.
[28] Anderson, M., et al., An experimental system for advanced heating, ventilating and air conditioning (HVAC) control. Energy and Buildings, 2007. 39(2): p. 136-147.
[29] Kaur, H. and D.S. Salaria, Bayesian regularization based neural network tool for software effort estimation. Global Journal of Computer Science and Technology, 2013.
[30] Jin, G.-Y., et al., A simple dynamic model of cooling coil unit. Energy Conversion and Management, 2006. 47(15-16): p. 2659-2672.
[31] Mustafaraj, G., G. Lowry, and J. Chen, Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural network models for an open office. Energy and Buildings, 2011. 43(6): p. 1452-1460.
[32] Xu, X., S. Wang, and G. Huang, Robust MPC for temperature control of air-conditioning systems concerning on constraints and multitype uncertainties. Building Services Engineering Research and Technology, 2010. 31(1): p. 39-55.
[33] Ruano, A.E., et al., Prediction of building's temperature using neural networks models. Energy and Buildings, 2006. 38(6): p. 682-694.
