The system has to be run on an external device as Python can’t run on the PLC directly. This device would need to be online at all times, as it will be contacted periodically, so some kind of server could be appropriate. The server needs to handle requests for setting flowline temperatures. It is important that the system can be manually overridden so the setpoint curve can be used at any time necessary, as requested by the County Council. Therefore, some sort of safety mechanism for overriding the controller has to be implemented.
References
[1] “netCDF4-Python: Python/NumPy interface to the netCDF C library,” accessed 2020-05-07. [Online]. Available: unidata.github.io/netcdf4-python/netCDF4/ index.html
[2] “Pyads, a python wrapper for TwinCAT’s ADS library.” [Online]. Available: https://github.com/stlehmann/pyads
[3] A. Afram and F. Janabi-Sharifi, “Theory and applications of hvac control systems – a review of model predictive control (mpc),” Building and Environment, vol. 72, pp. 343 – 355, 2014. [Online]. Available: http: //www.sciencedirect.com/science/article/pii/S0360132313003363
[4] A. Afram, F. Janabi-Sharifi, A. S. Fung, and K. Raahemifar, “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,”
Energy and Buildings, vol. 141, pp. 96 – 113, 2017. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0378778816310799
References
[5] P. Carbonnelle. (2020) Pypl popularity of programming language. Accessed 2020-04-27. [Online]. Available: http://pypl.github.io/PYPL.html
[6] G. Dreyfus,Neural Networks. Springer, 2005.
[7] G. Dreyfus, “Principles and model design methodology,” p. 127, 2005.
[8] Eclipse Foundation, “Eclipse Paho - MQTT and MQTT-SN software,” 2015. [Online]. Available: https://www.eclipse.org/paho/
[9] P. Ferreira, A. Ruano, S. Silva, and E. Conceic¸˜ao, “Neural networks based predictive control for thermal comfort and energy savings in public buildings,” Energy and Buildings, vol. 55, pp. 238 – 251, 2012, cool Roofs, Cool Pavements, Cool Cities, and Cool World. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S037877881200388X
[10] G. F. Franklin, J. D. Powell, and A. Emami-Naeini,Feedback Control of Dynamic Systems. Pearson Education Limited, 2014.
[11] G. Gholamibozanjani, J. Tarragona, A. de Gracia, C. Fern´andez, L. F. Cabeza, and M. M. Farid, “Model predictive control strategy applied to different types of building for space heating,” Applied Energy, vol. 231, pp. 959 – 971, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S0306261918314995
[12] M. Giftthaler, M. Neunert, M. St¨auble, and J. Buchli, “The control toolbox — an open-source c++ library for robotics, optimal and model predictive control,”2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), pp. 123–129, 2018.
[13] D. H. Hanssen, Programmable logic controllers: a practical approach to IEC 61131-3 using CODESYS. John Wiley Sons, 2015.
[14] H. Huang, L. Chen, and E. Hu, “A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings,”
Energy and Buildings, vol. 97, pp. 86 – 97, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0378778815002546
[15] A. Khamis, Z. Ismail, K. Haron, and A. T. Mohammed, “The effects of outliers data on neural network performance,” Journal of Applied Sciences, pp. 1394– 1398, 2005.
[16] P. Kim, “Matlab deep learning: With machine learning, neural networks and arti- ficial intelligence,” pp. 20 – 27, 2017.
References
[17] A. Larm´erus, “Styrning av v¨armesystem i kontorsbyggnader: J¨amf¨orelse mellan prognosstyrning, styrning som utnyttjar byggnadens v¨armetr¨oghet, samt traditionell styrning,” 2014. [Online]. Available: http://www.diva- portal.se/smash/get/diva2:726976/FULLTEXT01.pdf
[18] H. Liu, Y. Wu, D. Lei, and B. Li, “Gender differences in physiological and psychological responses to the thermal environment with varying clothing ensembles,” Building and Environment, vol. 141, pp. 45 – 54, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S036013231830307X [19] P. Martin, C. Barranquero, J. Sanchez, G. Gestoso, J. Andres, D. Refoyo, C. Garcia, A. Fernadez, and R. Lopez, “Opennn: Open neural network library,” 2019. [Online]. Available: https://www.opennn.net/
[20] V. Masson-Delmotte, P. Zhai, H.-O. P¨ortner, D. Roberts, J. Skea, P. Shukla, A. Pirani, W. Moufouma-Okia, C. P´ean, R. Pidcock, S. Connors, J. Matthews, Y. Chen, X. Zhou, M. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield. (2018) Global Warming of 1.5◦C. An IPCC Special Report on the impacts of global warming of 1.5◦C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Accessed 2020-04-07. [Online]. Available: https://www.ipcc. ch/site/assets/uploads/sites/2/2019/06/SR15 Full Report High Res.pdf
[21] W. McKinney,Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython. O’Reilly, 2012.
[22] P.-D. Moros¸an, R. Bourdais, D. Dumur, and J. Buisson, “Building temperature regulation using a distributed model predictive control,” Energy and Buildings, vol. 42, no. 9, pp. 1445 – 1452, 2010. [Online]. Available: http: //www.sciencedirect.com/science/article/pii/S0378778810000915
[23] Naturv˚ardsverket. (2019) Sveriges klimatm˚al och klimatpolitiska ramverk. Naturv˚ardsverket. Accessed 2020-04-14. [Online]. Available: https://www.naturvardsverket.se/Miljoarbete-i-samhallet/Miljoarbete-i-Sverige/ Uppdelat-efter-omrade/Klimat/Sveriges-klimatlag-och-klimatpolitiska-ramverk/ [24] P. Nejat, F. Jomehzadeh, M. M. Taheri, M. Gohari, and M. Z. A. Majid, “A
global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries),”Renewable and Sustainable Energy Reviews, vol. 43, pp. 843 – 862, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1364032114010053
References
[25] M. Norway. MET Norway Thredds Service at Norwegian Meteorological Institute. Accessed 2020-05-07. [Online]. Available: https://thredds.met.no/ thredds/metno.html
[26] B. Olesen and K. Parsons, “Introduction to thermal comfort standards and to the proposed new version of EN ISO 7730,” Energy and Buildings, vol. 34, no. 6, pp. 537 – 548, 2002, special Issue on Thermal Comfort Standards. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S037877880200004X [27] T. Oliphant, “NumPy: A guide to NumPy,” USA: Trelgol Publishing, 2006–,
accessed 2020-05-11. [Online]. Available: http://www.numpy.org/
[28] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Ma- chine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[29] Y. Peng, Z. Nagy, and A. Schl¨uter, “Temperature-preference learning with neural networks for occupant-centric building indoor climate controls,”Building and Environment, vol. 154, pp. 296 – 308, 2019. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0360132319300460
[30] Peter Van Overschee and Bart De Moor, “Rapid: The end of heuristic pid tuning,” IFAC Proceedings Volumes, vol. 33, no. 4, pp. 595 – 600, 2000, iFAC Workshop on Digital Control: Past, Present and Future of PID Control, Terrassa, Spain, 5-7 April 2000. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1474667017383088
[31] S. Purwar, I. N. Kar, and A. N. Jha, “Nonlinear system identification using neural networks,”IETE Journal of Research, vol. 53, no. 1, pp. 35–42, 2007. [Online]. Available: https://doi.org/10.1080/03772063.2007.10876119
[32] L. P´erez-Lombard, J. Ortiz, and C. Pout, “A review on buildings energy consumption information,” Energy and Buildings, vol. 40, no. 3, pp. 394 – 398, 2008. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S0378778807001016
[33] J. Ryd´en,Stokastik F¨or Ingenj¨orer. Studentlitteratur, 2015.
[34] M. Shanker, M. Hu, and M. Hung, “Effect of data standardization on neural network training,” Omega, vol. 24, no. 4, pp. 385 – 397, 1996. [Online]. Available: http://www.sciencedirect.com/science/article/pii/0305048396000102
References
[35] S. C. Sugarman, HVAC Fundamentals (3rd Edition). Fairmont Press, Inc., 2016. [Online]. Available: https://app.knovel.com/hotlink/khtml/id: kt010WWYN5/hvac-fundamentals-3rd/heating-ventilating-air
[36] Swedish Meteorological and Hydrological Institute. (2020) Meteorol- ogisk prognosmodell pmp3g (2,8 km uppl¨osning) - api. Accessed 2020-05-06. [Online]. Available: https://www.smhi.se/data/utforskaren-oppna- data/meteorologisk-prognosmodell-pmp3g-2-8-km-upplosning-api
[37] S. Zare, N. Hasheminezhad, K. Sarebanzadeh, F. Zolala, R. Hemmatjo, and D. Hassanvand, “Assessing thermal comfort in tourist attractions through objective and subjective procedures based on ISO 7730 standard: A field study,” Urban Climate, vol. 26, pp. 1 – 9, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2212095518301603
[38] K. ˚Astr¨om and T. H¨agglund, “The future of pid control,” Control Engineering Practice, vol. 9, no. 11, pp. 1163 – 1175, 2001. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0967066101000624
A Simulated Results
A
Simulated Results
10 Hour Forecast, April 12th 2019, 12:00 Time Radiation (W/m2) Outside temperature (◦C)
12:00 701 4,9 13:00 216 5,5 14:00 503 6,0 15:00 684 4,7 16:00 186 4,7 17:00 237 3,7 18:00 164 2,4 19:00 34 1,4 20:00 5 0,5 21:00 0 -0,3 22:00 0 -1,9
Predictions from 23◦C Predictions from 19◦C Predictions from 21◦C Time Flowline◦C Room◦C Flowline◦C Room◦C Flowline◦C Room◦C
12:00 26,2 23.0 52,6 19,0 39,4 21,0 13:00 43,9 21,0 43,3 21,0 43,7 21,0 14:00 43,4 21,0 43,0 21,0 43,2 21,0 15:00 45,3 21,0 44,8 21,0 45,1 21,0 16:00 44,9 21,0 44,4 21,0 44,7 21,0 17:00 46,2 21,0 45,7 21,0 46,0 21,0 18:00 47,7 21,0 47,2 21,0 47,5 21,0 19:00 48,8 21,0 48,3 21,0 48,6 21,0 20:00 49,9 21,0 49,3 21,0 49,7 21,0 21:00 45,8 21,0 35,0 21,0 44,0 21,0 22:00 32,5 21,0 21,5 21,0 28,0 21,0 23:00 - 21,0 - 21,0 - 21,0
Table 1Simulated results for April 12th, 2019, from 13:00 to 23:00. The two forecast columns are based on historical data from the weather station at the Uppsala University Hospital campus. The predicted flowline temperature is the result of the ANN-MPC, and the inside temperature is the ANNs prediction given the flowline temperature set last hour. There are three scenarios in where the room starts at 23◦C, 19◦C and 21◦C. Here, the controller is requested to hold 21◦C, without setting flowline to above 70◦C or below 13◦C.
A Simulated Results
10 Hour Forecast, October 10th 2019, 12:00 Time Radiation (W/m2) Outside temperature (◦C)
09:00 45 8,8 10:00 77 8,9 11:00 93 9,9 12:00 76 10,7 13:00 422 10,7 14:00 94 10,6 15:00 88 10,4 16:00 58 9,8 17:00 15 9,5 18:00 2 9,3 19:00 0 9,2
Predictions from 23◦C Predictions from 19◦C Predictions from 21◦C Time Flowline◦C Room◦C Flowline◦C Room◦C Flowline◦C Room◦C
09:00 13,0 23,0 64,5 19,0 35,4 21,0 10:00 32,7 21,2 39,9 21,0 35,3 21,0 11:00 33,6 21,0 38,5 21,0 33,3 21,0 12:00 32,1 21,0 37,4 21,0 31,7 21,0 13:00 31,1 21,0 37,6 21,0 30,7 21,0 14:00 32,2 21,0 37,4 21,0 31,8 21,0 15:00 32,7 21,0 37,5 21,0 32,8 21,0 16:00 34,1 21,0 38,1 21,0 34,9 21,0 17:00 34,9 21,0 38,3 21,0 36,4 21,0 18:00 35,3 21,0 38,5 21,0 38,0 21,0 19:00 35,6 21,0 38,3 21,0 39,4 21,0 20:00 - 21,0 - 21,0 - 21,0
Table 2Simulated results for October 10th, 2019, from 10:00 to 20:00. The two forecast columns are based on historical data from the weather station at the Uppsala University Hospital campus. The predicted flowline temperature is the result of the ANN-MPC, and the inside temperature is the ANNs prediction given the flowline temperature set last hour. There are three scenarios in where the room starts at 23◦C, 19◦C and 21◦C. The controller is requested to hold 21◦C, without setting flowline to above 70◦C or below 13◦C.