Controlling HVAC and lighting systems

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Buildings contribute to roughly 40% of the global energy use (approx. 64 PWh), of which a large portion is used for heating, cooling, ventilation, and air-conditioning (HVAC).[1] The challenge is that current building management systems (BMS) are manually designed by human operators, which leads to energy inefficient operations. ML can help develop predictive models of building energy systems, leading to efficient building operation via advanced model-based optimal control methods. Independent studies reported that advanced HVAC control can significantly reduce energy use and mitigate greenhouse gas emissions with energy savings of 13% to 53%[2][3][4][5][6]. Additional benefit is that advanced control strategies can provide grid flexibility services via price-responsiveness and active demand response capabilities[7][8][9][10][11][12].

Background Readings[edit | edit source]

Community[edit | edit source]

Libraries and Tools[edit | edit source]

Data[edit | edit source]

Grey Bricks Buildings: An open source data set of 225 calibrated Dutch residential building heat models, comprising identified models and their respective forward selection paths, estimated RC parameters, building thermal properties, i.e., heat transfer coefficient, as well as reported building meta-data.

Future Directions[edit | edit source]

Based on the reflections presented in[1][13][14][15][16], there are several challenges for wide-spread application of advanced building controls:

  1. Availability of appropriate hardware and software infrastructure with compatible communication interfaces.
  2. Availability of data, and data-efficiency of the modeling and control design algorithms.
  3. User-friendly and computationally efficient building modeling and control design tools.
  4. Automated design, tuning, and deployment with plug-and-play implementation of advanced control strategy.
  5. Capability of handling multi-dimensional and hierarchical systems with dynamical coupling.
  6. Dealing with uncertainties in modeling and real-time measurements.
  7. Control security/safety/robustness by minimizing or eliminating the chance of generating control signals that might lead to undesirable outcomes during operation.
  8. Trained personnel to handle commissioning, and maintenance of advanced control methods in practice.
  9. Privacy and cyber-security issues and the user trust.
  10. Performance evaluation and algorithms benchmarking.

References[edit | edit source]

  1. 1.0 1.1 "All you need to know about model predictive control for buildings". Annual Reviews in Control. 2020-09-29. doi:10.1016/j.arcontrol.2020.09.001. ISSN 1367-5788.
  2. Gyalistras, Dimitrios; Gwerder, Markus; Oldewurtel, Frauke; Jones, Colin N.; Morari, Manfred; Lehmann, Beat; Wirth, Katharina; Stauch, Vanessa (2010). "Analysis of energy savings potentials for integrated room automation". Clima 2010 sustainable energy use in buildings : 10th REHVA world congress ; 9 - 12 May, Antalya ; proceedings. Turkish Society of HVAC and Sanitary Engineers (TTMD); Federation of European Heating and Air Conditioning Associations (REHVA): R2. ISBN 978-975-6907-14-6.
  3. Castilla, María del Mar; Álvarez, José Domingo; Rodríguez, Francisco; Berenguel, Manuel (2014). "Comfort Control in Buildings". Advances in Industrial Control. doi:10.1007/978-1-4471-6347-3. ISSN 1430-9491.
  4. Široký, Jan; Oldewurtel, Frauke; Cigler, Jiří; Prívara, Samuel (2011-09-01). "Experimental analysis of model predictive control for an energy efficient building heating system". Applied Energy. 88 (9): 3079–3087. doi:10.1016/j.apenergy.2011.03.009. ISSN 0306-2619.
  5. Ma, Y.; Borrelli, F.; Hencey, B.; Coffey, B.; Bengea, S.; Haves, P. (2012-05). "Model Predictive Control for the Operation of Building Cooling Systems". IEEE Transactions on Control Systems Technology. 20 (3): 796–803. doi:10.1109/TCST.2011.2124461. ISSN 1558-0865. Check date values in: |date= (help)
  6. "Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration". Journal of Process Control. 88: 63–77. 2020-04-01. doi:10.1016/j.jprocont.2020.02.007. ISSN 0959-1524.
  7. Bianchini, G.; Casini, M.; Pepe, D.; Vicino, A.; Zanvettor, G. G. (2017-12). "An integrated MPC approach for demand-response heating and energy storage operation in smart buildings". 2017 IEEE 56th Annual Conference on Decision and Control (CDC): 3865–3870. doi:10.1109/CDC.2017.8264228. Check date values in: |date= (help)
  8. Borsche, Theodor; Oldewurtel, Frauke; Andersson, Göran (2014-01-01). "Scenario-based MPC for Energy Schedule Compliance with Demand Response". IFAC Proceedings Volumes. 19th IFAC World Congress. 47 (3): 10299–10304. doi:10.3182/20140824-6-ZA-1003.01284. ISSN 1474-6670.
  9. Esther, B. Priya; Kumar, K. Sathish (2016-06-01). "A survey on residential Demand Side Management architecture, approaches, optimization models and methods". Renewable and Sustainable Energy Reviews. 59: 342–351. doi:10.1016/j.rser.2015.12.282. ISSN 1364-0321.
  10. O'Neill, D.; Levorato, M.; Goldsmith, A.; Mitra, U. (2010-10). "Residential Demand Response Using Reinforcement Learning". 2010 First IEEE International Conference on Smart Grid Communications: 409–414. doi:10.1109/SMARTGRID.2010.5622078. Check date values in: |date= (help)
  11. Pallonetto, Fabiano; De Rosa, Mattia; Milano, Federico; Finn, Donal P. (2019-04-01). "Demand response algorithms for smart-grid ready residential buildings using machine learning models". Applied Energy. 239: 1265–1282. doi:10.1016/j.apenergy.2019.02.020. ISSN 0306-2619.
  12. Vázquez-Canteli, José R.; Nagy, Zoltán (2019-02-01). "Reinforcement learning for demand response: A review of algorithms and modeling techniques". Applied Energy. 235: 1072–1089. doi:10.1016/j.apenergy.2018.11.002. ISSN 0306-2619.
  13. Prívara, Samuel; Cigler, Jiří; Váňa, Zdeněk; Oldewurtel, Frauke; Sagerschnig, Carina; Žáčeková, Eva (2013-01-01). "Building modeling as a crucial part for building predictive control". Energy and Buildings. 56: 8–22. doi:10.1016/j.enbuild.2012.10.024. ISSN 0378-7788.
  14. Killian, M.; Kozek, M. (2016-08-15). "Ten questions concerning model predictive control for energy efficient buildings". Building and Environment. 105: 403–412. doi:10.1016/j.buildenv.2016.05.034. ISSN 0360-1323.
  15. Henze, Gregor P. (2013-05-01). "Model predictive control for buildings: a quantum leap?". Journal of Building Performance Simulation. 6 (3): 157–158. doi:10.1080/19401493.2013.778519. ISSN 1940-1493.
  16. Wang, Zhe; Hong, Tianzhen (2020-07). "Reinforcement learning for building controls: The opportunities and challenges". Applied Energy. 269: 115036. doi:10.1016/j.apenergy.2020.115036. ISSN 0306-2619. Check date values in: |date= (help)