کنترل شرایط محیطی داخل ساختمان بر مبنای مدل و استفاده از روش کنترل پیش‌بین
محورهای موضوعی : انرژی های تجدیدپذیر
امیررضا علی زاده
1
,
سید محمد کارگر
2
1 - دانشکده مهندسی برق- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
2 - مرکز تحقیقات ریز شبکه های هوشمند- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
کلید واژه: بهینه‌سازی مصرف انرژی, شناسایی سیستم, سیستم‌های چندورودی-چندخروجی, سیستم‌های گرمایش, سرمایش و تهویه‌مطبوع, کنترل پیش‌بین مبتنی بر مدل, تحلیل زیرفضا,
چکیده مقاله :
در این مقاله یک روش کنترلی با رویکرد کنترل پیش بین مبتنی بر مدل به منظور تنظیم دمای داخل ساختمان ارائه می گردد. در سال های اخیر بیشترین میزان مصرف انرژی در ساختمان ها، مربوط به سیستم های گرمایش، سرمایش و تهویه مطبوع بوده است. از همین رو کنترل سیستم های گرمایش، سرمایش و تهویه مطبوع در ساختمان ها در راستای کاهش مصرف انرژی مورد توجه قرار گرفته است. در ابتدا یک مدل ساختمانی در نرم افزار انرژی پلاس طراحی و سپس تمام داده های ورودی و خروجی، جهت شناسایی از این نرم افزار استخراج می شوند. در ادامه شناسایی سیستم به روش مدل فضای حالت انجام می گیرد. سپس کنترل کننده با رویکرد پیش بین جهت کنترل دمای داخلی ساختمان طراحی می گردد. نوآوری این مقاله در دو زمینه قابل بیان است، اول اینکه برخلاف اکثر پژوهش های صورت گرفته، داده های استفاده شده در قسمت شناسایی سیستم با فرض ایزوله نبودن اتاق ها و وجود ارتباط دمایی بین اتاق ها انجام شده است که باعث تولید مدلی دقیق تر از سیستم می گردد. دوماً در این پژوهش اثر دمای خارجی محیط به عنوان اغتشاش در نظر گرفته شده است و تأثیر آن در طراحی کنترل کننده مورد بررسی قرار گرفته است. در پایان،نتایج به دست آمده از شبیه سازی در افق یک ساعته، عملکرد خوب کنترل کننده ی پیش بین مبتنی بر مدل نسبت به روش کنترل بهینه به همراه کاهش مصرف انرژی در کنار حفظ شرایط مطلوب دمایی برای ساکنین در یک 24 ساعت را نشان می دهد.
In this paper, a model predictive control approach is presented to regulate indoor temperature. In recent years, the highest energy consumption in buildings is related to heating, ventilation, and air conditioning systems. Therefore, the control of heating, ventilation, and air conditioning systems in buildings has been taken into consideration to reduce energy consumption. At first, a construction model is designed in the Energy-plus software, then all input and output data is collected from this software to identify the state-space model. Then the Model-based predictive control algorithm is applied to control the indoor building temperature. The contribution of this paper is two-fold. Firstly, the data used in the system identification section is based on the assumption that the rooms are not isolated. There is a temperature relationship between the rooms, which provides a more realistic model of the system. Secondly, the external ambient temperature is considered as a disturbance, and its effect on controller design has been investigated. The simulation results for 24 hours show the good performance of the model predictive control approach over the optimal control method along with reducing energy consumption while maintaining the optimal temperature conditions.
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_||_[1] M. Movahedpour, S. Mohammadi, M. Kiani, T. Niknam, M. Zadehbagheri, “Optimal design of residential microgrids with regard to fault occurrence and possibility of power outage”, Journal of Intelligent Procedures in Electrical Technology, vol. 10, no. 39, pp. 29-44, Autumn 2019 (in Persian).
[2] M. Mahdavian N. Behzadfar “A review of wind energy conversion system and application of various induction generators”, Journal of Novel Researches on Electrical Power, vol. 8, no. 4, pp. 55-66, Winter 2020 (in Persian).
[3] M. A. Praprost, "Investigating energyplus as a simulation tool for deploying VOLTTRON transactive energy technologies in commercial buildings", Case Western Reserve University, pp. 1-153, May 2018 (doi: 0000-0001-7463-8427).
[4] L. Pérez-Lombard, J. Ortiz, C. Pout, "A review on buildings energy consumption information", Energy and Buildings, vol. 40, no. 3, pp. 394-398, Jan. 2008 (doi: 10.1016/j.enbuild.2007.03.007).
[5] K. D. Kolokotsa, A. Pouliezos, G. Stavrakakis, C. Lazos, "Predictive control techniques for energy and indoor environmental quality management in buildings", Building and Environment,vol. 44, no. 9, pp. 1850-1863, Sept. 2009 (doi: 10.1016/j.buildenv.2008.12.007).
[6] J. Ma, J. Qin, T. Salsbury, P. Xu, "Demand reduction in building energy systems based on economic model predictive control", Chemical Engineering Science, vol. 67, no. 1, pp. 92-100, Jan. 2012 (doi: 10.1016/j.ces.2011.07.052).
[7] J. Ma, J. Qin, T. Salsbury, "Economic model predictive control for building energy systems", Proceeding of the IEEE/ICC, pp. 1-6, Kanpur, India, Jan. 2011 (doi: 10.1109/ISGT.2011.5759140).
[8] S. Royer, S. Thil, T. Talbert, M. Polit, "A procedure for modeling buildings and their thermal zones using co-simulation and system identification", Energy and Buildings, vol. 78, pp. 231-237, Aug. 2014 (doi: 10.1016/j.enbuild.2014.04.013).
[9] T. Q. Péan, J. Salom, R. Costa-Castelló, "Review of control strategies for improving the energy flexibility provided by heat pump systems in buildings", Journal of Process Control, vol. 74, pp. 35-49, Feb. 2019 (doi: 10.1016/j.jprocont.2018.03.006).
[10] D. H. Blum, K. Arendt, L. Rivalin, M. A. Piette, M. Wetter, C. T. Veje, "Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems", Applied Energy, vol. 236, pp. 410-425, Feb. 2019 (doi: 10.1016/j.apenergy.2018.11.093).
[11] A. Ryzhov, H. Ouerdane, E. Gryazina, A. Bischi, K. Turitsyn, "Model predictive control of indoor microclimate: existing building stock comfort improvement", Energy Conversion and Management, vol. 179, pp. 219-228, Jan. 2019 (doi: 10.1016/j.enconman.2018.10.046).
[12] M. J. Bursill, L. O'Brien, I. B. Morrison, "Morrison, multi-zone field study of rule extraction control to simplify implementation of predictive control to reduce building energy use", Energy and Buildings, vol. 222, Article: 110056, Sept. 2020 (doi: 10.1016/j.enbuild.2020.110056).
[13] J. Wei, Y.J. Zhang, "Exploring a strategy for tall office buildings based on thermal energy consumption from industrialized perspective: an empirical study in china", Journal of Cleaner Production, vol. 257, Article: 120497, June 2020 (doi: 10.1016/j.jclepro.2020.120497).
[14] M. D. Knudsen, S. Petersen, "Economic model predictive control of space heating and dynamic solar shading", Energy and Buildings, vol. 209, Article: 109661, Feb. 2020 (doi: 10.1016/j.enbuild.2019.109661).
[15] L. K. Ganjali-khani, F. Sheikholeslam, H. Mahdavi-Nasab, “System identification of a nonlinear multivariable steam generator power plant using time delay and wavelet neural networks”, Journal of Intelligent Procedures in Electrical Technology, vol. 3, no. 12, pp. 67-73, Winter 2013 (in Persian).
[16] R. Pirmoradi, S. M. Kargar, A. Zare-Bidaki, “Modeling distillation column using ARX model structure and artificial neural networks”, Journal of Intelligent Procedures in Electrical Technology, vol. 3, no. 10, pp. 66-71, Spring 2013 (in Persian).
[17] J. Swigart, S. Lall, "An explicit state-space solution for a decentralized two-player optimal linear-quadratic regulator", Proceedings of the IEEE/ACC, pp. 6385-6390, Baltimore, MD, USA, June 2010 (doi: 10.1109/ACC.2010.5531482).