طراحی سیستم کنترل هوشمند جدید جهت تهویه هوای ساختمان به منظور کاهش اتلاف انرژی
محورهای موضوعی :
انرژی های تجدید پذیر
جعفر طاوسی
1
,
مجید ولیزاده
2
1 - استادیار، دانشکده فنی و مهندسی، دانشگاه ایلام، ایلام، ایران. *(مسوول مکاتبات)
2 - استادیار، دانشکده فنی و مهندسی، دانشگاه ایلام، ایلام، ایران
تاریخ دریافت : 1398/09/02
تاریخ پذیرش : 1399/05/05
تاریخ انتشار : 1400/06/01
کلید واژه:
کاهش اتلاف انرژی,
کنترل هوشمند,
فن‌کویل آب-خنک,
چکیده مقاله :
زمینه و هدف: امروزه به دلیل رشد جمعیت، نیاز روزافزون بشر و افزایش دمای سالیانه کره زمین، مصرف انرژی همواره روبه افزیش است. یکی از مصرف کنندگان عمده انرژی، ساختمان های با زیربنای بالای 2000 مترمربع هستند. بنابراین کاهش مصرف انرژی در ساختمان‌ها بایستی بسیار مورد توجه قرار گیرد. به عبارت دیگر، هدف از این مقاله کنترل سیستم سرمایش در تابستان و کنترل سیستم گرمایش در زمستان است به نحوی که وضعیت دما و رطوبت کنونی اتاق در نظر گرفته شود و جهت اینکار از منطق فازی استفاده می شود.روش بررسی: در این تحقیق، کنترل منطق فازی برای سیستم تهویه هوای ساختمان جهت افزایش بازده انرژی و تامین محیط راحت بررسی شده است. یک مدل تئوریک از واحد فن‌کویل[1] و انتقال حرارت بین هوا و سیال خنک‌کننده استخراج می گردد. متغیرهای کنترلی، دمای اتاق و رطوبت نسبی و نتایج کنترلی، درصد نرخ جریان آب خنک و گرم شده در تابستان و درصد نرخ جریان آب داغ و بخار تزریقی در زمستان هستند.یافته ها: در این پژوهش متوجه شدیم که با استفاده از کنترل کننده هوشمند و منطبق بر سیستم فازی، می توان تا بالای 90% از اتلاف انرژی جلوگیری کرد.بحث و نتیجه گیری: نتایج کنترل فازی با کنترل متداول تناسبی-انتگرال‌گیر-مشتق‌گیر[2] مقایسه می گردد. ثابت می شود که کنترل‌کننده‌ی فازی کارایی بیشتری داشته و موجب مصرف انرژی کمتری در مقایسه با کنترل PID است.[1]- Fan Coil Unit (FCU)[2]- Proportional Integral Derivative (PID)
چکیده انگلیسی:
Background and Objective: Today, energy consumption is on the rise due to population growth, growing human needs, and rising global temperatures. One of the major consumers of energy is buildings with an infrastructure of more than 2,000 square meters. Therefore, reducing energy consumption in buildings should be given much attention. In other words, the purpose of this paper is to control the cooling system in summer and to control the heating system in winter so that the current temperature and humidity of the room are taken into account.Material and Methodology:In this study, fuzzy logic control for building air conditioning system to increase energy efficiency and provide a comfortable environment has been investigated. A theoretical model is extracted from the fan coil unit and the heat transfer between the air and the cooling fluid. The control variables are room temperature and relative humidity and control results, the percentage of cooled and heated water flow rate in summer and the percentage of hot water flow rate and injection steam in winter.Findings: In this study, we found that by using an intelligent controller and compatible with the fuzzy system, up to 90% of energy loss can be prevented.Discussion and Conclusion: Fuzzy control results are compared with conventional proportional-integral-derivative control. Fuzzy controllers are proven to be more efficient and consume less energy than PID controls.
منابع و مأخذ:
He, X., Chen, S., Lv, X., Kim, E.J. 2015. Simplified Model of HVAC Load Prediction for Urban Building Districts, Procedia Engineering, Vol. 121, pp. 167-174.
Majidzadeh M. 2019. Development of a Modified Energy Saving Glass for Energy Management of Air Conditioning System and Transmission Improvement of In-Service Frequency Bands. JEM. 9 (1), pp. 48-55. (In Persian)
Khodakarami J, Ghobadi P. 2016. Optimizing of Energy Consumption in an Office Building Equipped with Intelligent Management System. JEM. 6 (2), pp.12-23. (In Persian)
Tavoosi, F. Mohammadi, 2019. Design a new intelligent control for a class of nonlinear systems, 6th International Conference on Control, Instrumentation and Automation (ICCIA), pp. 1-5.
Ahmadzadeh talatapeh, M. 2017. Application of Solar Thermal Collectors to Improve the Energy Performance of the Fresh Air HVAC Systems, JEM. Vol. 6, No. 4, pp.:44-53.
Zaheer-uddin, A. M., Zheng, G.R., 2000. Optimal Control of Time Scheduled Heating, Ventilating and Air Conditioning Processes in Buildings, Energy Convers. Manage, Vol. 41, pp. 49-60.
Srivastava, C., Yang, Z., Jain, R. K. 2019. Understanding the Adoption and Usage of Data Analytics and Simulation among Building Energy Management Professionals: A Nationwide Survey, Building and Environment, Vol. 157, pp. 139-164.
Latif, M., Nasir, A. 2019. Decentralized Stochastic Control for Building Energy and Comfort Management, Journal of Building Engineering, Vol. 24.
Yoon, S.H., Kim, S.Y., Park, G.H., Kim, Y.K., Cho, C.H., Park, B.H. 2018. Multiple Power-Based Building Energy Management System for Efficient Management of Building Energy, Sustainable Cities and Society, Vol. 42, pp. 462-470.
Liu, H., Chen, C., Lv, X., Wu, X., Liu, M. 2019. Deterministic Wind Energy Forecasting: A Review of Intelligent Predictors and Auxiliary Methods, Energy Conversion and Management, Vol. 195, pp. 328-345.
Khalid, R., Javaid, N., Rahim, M.H., Aslam, S., Sher, A. 2019. Fuzzy Energy Management Controller and Scheduler for Smart Homes, Sustainable Computing: Informatics and Systems, Vol. 21, pp. 103-118.
Sharifi, A.H., Maghouli, P. 2019. Energy Management of Smart Homes Equipped With Energy Storage Systems Considering the PAR Index Based on Real-Time Pricing, Sustainable Cities and Society, Vol. 45, pp. 579-587.
Pour Asad, A. Shamsi, H. Ivani, and J. Tavoosi. 2016. Adaptive Intelligent Inverse Control of Nonlinear Systems with Regard to Sensor Noise and Parameter Uncertainty (Magnetic Ball Levitation System Case Study), International Journal on Smart Sensing and Intelligent Systems, Vol. 9(1), pp. 148-169.
Pour Asad, A. Shamsi, and J. Tavoosi. 2017. Backstepping-Based Recurrent Type-2 Fuzzy Sliding Mode Control for MIMO Systems (MEMS Triaxial Gyroscope Case Study), International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 25(2), pp. 213-233.
J Tavoosi, M Alaei, B Jahani. 2011. Neuro–Fuzzy Controller for Position Control of Robot Arm", 5th Symposium on Advance in Science & Technology.
J Tavoosi, R Azami. 2019. A New Method for Controlling the Speed of a Surface Permanent Magnet Synchronous Motor using Fuzzy Comparative Controller with Hybrid Learning, Computational Intelligence in Electrical Engineering, 10(3), pp. 57-68.
Shahnazari, H., Mhaskar, P., House, J.M., Salsbury,T.I. 2019. Modeling and Fault Diagnosis Design for HVAC Systems Using Recurrent Neural Networks, Computers & Chemical Engineering, Vol. 126, pp. 189-203.
Ruano, A.E., Ferreira, P.M. 2014. Neural Network Based HVAC Predictive Control, IFAC Proceedings Volumes, Vol. 47, No. 3, pp. 3617-3622.
Attaran, S.M., Yusof, R., Selamat, H. 2016. A Novel Optimization Algorithm Based on Epsilon Constraint-RBF Neural Network for Tuning PID Controller in Decoupled HVAC System, Applied Thermal Engineering, Vol. 99, pp. 613-624.
MBB Sharifian, A Mirlo, J Tavoosi, M Sabahi, Self-adaptive RBF neural network PID controller in linear elevator, International Conference on Electrical Machines and Systems, 2011.
Tavoosi, A. A. Suratgar, and M. B. Menhaj. 2016. Nonlinear System Identification Based on a Self-Organizing Type-2 Fuzzy RBFN, Engineering Applications of Artificial Intelligence, Vol. 54, pp. 26-38.
Tavoosi, A. A. Suratgar, and M. B. Menhaj. 2016. Stable ANFIS2 for Nonlinear System Identification, Neurocomputing, Vol. 182, pp. 235-246.
Tavoosi, A. A. Suratgar, and M. B. Menhaj. 2017. Stability Analysis of Recurrent Type-2 TSK Fuzzy Systems with Nonlinear Consequent Part, Neural Computing and Applications, Vol. 28(1), pp. 47-56.
Tavoosi, A. A. Suratgar, and M. B. Menhaj. 2017. Stability Analysis of a Class of MIMO Recurrent Type-2 Fuzzy Systems, International Journal of Fuzzy Systems, Vol. 19(3), pp. 895–908.
Tavoosi, and M. A. Badamchizadeh. 2013. A Class of Type-2 Fuzzy Neural Networks for Nonlinear Dynamical System Identification, Neural Computing and Applications, Vol. 23(3-4), pp. 707–717.
MS Hesarian, J Tavoosi. 2019. Green Technology used in Finishing Process Study of the Wrinkled Cotton Fabric by Radial Basis Function neurons (Experimental and Modeling analysis), Advances in Environmental Technology, Vol. 5 (1), pp. 35-45.
Sekhar, C., Anand, P., Schiavon, S., Tham, K.W., Cheong, D., Saber, E.M. 2018. Adaptable Cooling Coil Performance during Part Loads in the Tropics—A Computational Evaluation, Energy and Buildings, 159, pp. 148-163.
Wu, Y., Chen, A., Luhung, I., Gall, E.T., Cao, Q., Chang, V.W.C., Nazaroff, W.W. 2016. Bioaerosol Deposition on an Air-Conditioning Cooling Coil, Atmospheric Environment, Vol. 144, pp. 257-265.
Cui, X., Yang, X., Qin, S., Meng, X., Jin, L., Chua, K.J. 2019. Performance Investigation of an Evaporative Pre-Cooled Air-Conditioning System in Tropics, Energy Procedia, Vol. 158, pp. 5673-5678.
J Tavoosi, M Alaei, B Jahani. Temperature Control of Water Bath by using Neuro-Fuzzy Controller", 5th Symposium on Advance in Science & Technology 2011.
J Tavoosi, M Alaei, B Jahani, MA Daneshwar. 2011. A novel intelligent control system design for water bath temperature control, Australian Journal of Basic and Applied Sciences 5 (12), 1879-1885.
J Tavoosi, MA Badamchizadeh, S Ghaemi. 2011. Adaptive Inverse Control of Nonlinear Dynamical System Using Type-2 Fuzzy Neural Networks, Journal of Control 5 (2), 52-60.
J Tavoosi. 2020. An experimental study on inverse adaptive neural fuzzy control for nonlinear systems, International Journal of Knowledge-based and Intelligent Engineering Systems, Vol. 24 (2), pp. 135-143.
J Tavoosi. 2020. Hybrid intelligent adaptive controller for tiltrotor UAV, International Journal of Intelligent Unmanned Systems, Vol. 9 (4), pp. 256-273.
J Tavoosi. 2020. PMSM speed control based on intelligent sliding mode technique, PMSM speed control based on intelligent sliding mode technique, Vol. 39 (6), pp. 1315-1328.
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He, X., Chen, S., Lv, X., Kim, E.J. 2015. Simplified Model of HVAC Load Prediction for Urban Building Districts, Procedia Engineering, Vol. 121, pp. 167-174.
Majidzadeh M. 2019. Development of a Modified Energy Saving Glass for Energy Management of Air Conditioning System and Transmission Improvement of In-Service Frequency Bands. JEM. 9 (1), pp. 48-55. (In Persian)
Khodakarami J, Ghobadi P. 2016. Optimizing of Energy Consumption in an Office Building Equipped with Intelligent Management System. JEM. 6 (2), pp.12-23. (In Persian)
Tavoosi, F. Mohammadi, 2019. Design a new intelligent control for a class of nonlinear systems, 6th International Conference on Control, Instrumentation and Automation (ICCIA), pp. 1-5.
Ahmadzadeh talatapeh, M. 2017. Application of Solar Thermal Collectors to Improve the Energy Performance of the Fresh Air HVAC Systems, JEM. Vol. 6, No. 4, pp.:44-53.
Zaheer-uddin, A. M., Zheng, G.R., 2000. Optimal Control of Time Scheduled Heating, Ventilating and Air Conditioning Processes in Buildings, Energy Convers. Manage, Vol. 41, pp. 49-60.
Srivastava, C., Yang, Z., Jain, R. K. 2019. Understanding the Adoption and Usage of Data Analytics and Simulation among Building Energy Management Professionals: A Nationwide Survey, Building and Environment, Vol. 157, pp. 139-164.
Latif, M., Nasir, A. 2019. Decentralized Stochastic Control for Building Energy and Comfort Management, Journal of Building Engineering, Vol. 24.
Yoon, S.H., Kim, S.Y., Park, G.H., Kim, Y.K., Cho, C.H., Park, B.H. 2018. Multiple Power-Based Building Energy Management System for Efficient Management of Building Energy, Sustainable Cities and Society, Vol. 42, pp. 462-470.
Liu, H., Chen, C., Lv, X., Wu, X., Liu, M. 2019. Deterministic Wind Energy Forecasting: A Review of Intelligent Predictors and Auxiliary Methods, Energy Conversion and Management, Vol. 195, pp. 328-345.
Khalid, R., Javaid, N., Rahim, M.H., Aslam, S., Sher, A. 2019. Fuzzy Energy Management Controller and Scheduler for Smart Homes, Sustainable Computing: Informatics and Systems, Vol. 21, pp. 103-118.
Sharifi, A.H., Maghouli, P. 2019. Energy Management of Smart Homes Equipped With Energy Storage Systems Considering the PAR Index Based on Real-Time Pricing, Sustainable Cities and Society, Vol. 45, pp. 579-587.
Pour Asad, A. Shamsi, H. Ivani, and J. Tavoosi. 2016. Adaptive Intelligent Inverse Control of Nonlinear Systems with Regard to Sensor Noise and Parameter Uncertainty (Magnetic Ball Levitation System Case Study), International Journal on Smart Sensing and Intelligent Systems, Vol. 9(1), pp. 148-169.
Pour Asad, A. Shamsi, and J. Tavoosi. 2017. Backstepping-Based Recurrent Type-2 Fuzzy Sliding Mode Control for MIMO Systems (MEMS Triaxial Gyroscope Case Study), International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 25(2), pp. 213-233.
J Tavoosi, M Alaei, B Jahani. 2011. Neuro–Fuzzy Controller for Position Control of Robot Arm", 5th Symposium on Advance in Science & Technology.
J Tavoosi, R Azami. 2019. A New Method for Controlling the Speed of a Surface Permanent Magnet Synchronous Motor using Fuzzy Comparative Controller with Hybrid Learning, Computational Intelligence in Electrical Engineering, 10(3), pp. 57-68.
Shahnazari, H., Mhaskar, P., House, J.M., Salsbury,T.I. 2019. Modeling and Fault Diagnosis Design for HVAC Systems Using Recurrent Neural Networks, Computers & Chemical Engineering, Vol. 126, pp. 189-203.
Ruano, A.E., Ferreira, P.M. 2014. Neural Network Based HVAC Predictive Control, IFAC Proceedings Volumes, Vol. 47, No. 3, pp. 3617-3622.
Attaran, S.M., Yusof, R., Selamat, H. 2016. A Novel Optimization Algorithm Based on Epsilon Constraint-RBF Neural Network for Tuning PID Controller in Decoupled HVAC System, Applied Thermal Engineering, Vol. 99, pp. 613-624.
MBB Sharifian, A Mirlo, J Tavoosi, M Sabahi, Self-adaptive RBF neural network PID controller in linear elevator, International Conference on Electrical Machines and Systems, 2011.
Tavoosi, A. A. Suratgar, and M. B. Menhaj. 2016. Nonlinear System Identification Based on a Self-Organizing Type-2 Fuzzy RBFN, Engineering Applications of Artificial Intelligence, Vol. 54, pp. 26-38.
Tavoosi, A. A. Suratgar, and M. B. Menhaj. 2016. Stable ANFIS2 for Nonlinear System Identification, Neurocomputing, Vol. 182, pp. 235-246.
Tavoosi, A. A. Suratgar, and M. B. Menhaj. 2017. Stability Analysis of Recurrent Type-2 TSK Fuzzy Systems with Nonlinear Consequent Part, Neural Computing and Applications, Vol. 28(1), pp. 47-56.
Tavoosi, A. A. Suratgar, and M. B. Menhaj. 2017. Stability Analysis of a Class of MIMO Recurrent Type-2 Fuzzy Systems, International Journal of Fuzzy Systems, Vol. 19(3), pp. 895–908.
Tavoosi, and M. A. Badamchizadeh. 2013. A Class of Type-2 Fuzzy Neural Networks for Nonlinear Dynamical System Identification, Neural Computing and Applications, Vol. 23(3-4), pp. 707–717.
MS Hesarian, J Tavoosi. 2019. Green Technology used in Finishing Process Study of the Wrinkled Cotton Fabric by Radial Basis Function neurons (Experimental and Modeling analysis), Advances in Environmental Technology, Vol. 5 (1), pp. 35-45.
Sekhar, C., Anand, P., Schiavon, S., Tham, K.W., Cheong, D., Saber, E.M. 2018. Adaptable Cooling Coil Performance during Part Loads in the Tropics—A Computational Evaluation, Energy and Buildings, 159, pp. 148-163.
Wu, Y., Chen, A., Luhung, I., Gall, E.T., Cao, Q., Chang, V.W.C., Nazaroff, W.W. 2016. Bioaerosol Deposition on an Air-Conditioning Cooling Coil, Atmospheric Environment, Vol. 144, pp. 257-265.
Cui, X., Yang, X., Qin, S., Meng, X., Jin, L., Chua, K.J. 2019. Performance Investigation of an Evaporative Pre-Cooled Air-Conditioning System in Tropics, Energy Procedia, Vol. 158, pp. 5673-5678.
J Tavoosi, M Alaei, B Jahani. Temperature Control of Water Bath by using Neuro-Fuzzy Controller", 5th Symposium on Advance in Science & Technology 2011.
J Tavoosi, M Alaei, B Jahani, MA Daneshwar. 2011. A novel intelligent control system design for water bath temperature control, Australian Journal of Basic and Applied Sciences 5 (12), 1879-1885.
J Tavoosi, MA Badamchizadeh, S Ghaemi. 2011. Adaptive Inverse Control of Nonlinear Dynamical System Using Type-2 Fuzzy Neural Networks, Journal of Control 5 (2), 52-60.
J Tavoosi. 2020. An experimental study on inverse adaptive neural fuzzy control for nonlinear systems, International Journal of Knowledge-based and Intelligent Engineering Systems, Vol. 24 (2), pp. 135-143.
J Tavoosi. 2020. Hybrid intelligent adaptive controller for tiltrotor UAV, International Journal of Intelligent Unmanned Systems, Vol. 9 (4), pp. 256-273.
J Tavoosi. 2020. PMSM speed control based on intelligent sliding mode technique, PMSM speed control based on intelligent sliding mode technique, Vol. 39 (6), pp. 1315-1328.
