Large Neighborhood Search for Energy Aware Meeting Scheduling in Smart Buildings

  • Boon Ping Lim
  • Menkes van den Briel
  • Sylvie Thiébaux
  • Russell Bent
  • Scott Backhaus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9075)

Abstract

One of the main inefficiencies in building management systems is the widespread use of schedule-based control when operating heating, ventilation and air conditioning (HVAC) systems. HVAC systems typically operate on a pre-designed schedule that heats or cools rooms in the building to a set temperature even when rooms are not being used. Occupants, however, influence the thermal behavior of buildings. As a result, using occupancy information for scheduling meetings to occur at specific times and in specific rooms has significant energy savings potential. As shown in Lim et al. [15], combining HVAC control with meeting scheduling can lead to substantial improvements in energy efficiency. We extend this work and develop an approach that scales to larger problems by combining mixed integer programming (MIP) with large neighborhood search (LNS). LNS is used to destroy part of the schedule and MIP is used to repair the schedule so as to minimize energy consumption. This approach is far more effective than solving the complete problem as a MIP problem. Our results show that solutions from the LNS-based approach are up to 36% better than the MIP-based approach when both given 15 minutes.

Keywords

Smart buildings Scheduling Large neighborhood search HVAC control 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    ASHRAE: ASHRAE handbook: Fundamentals. American Society of Heating, Refrigerating and Air-Conditioning Engineers (2013)Google Scholar
  2. 2.
    Bloomfield, D., Fisk, D.: The optimisation of intermittent heating. Building and Environment 12(1), 43–55 (1977)CrossRefGoogle Scholar
  3. 3.
    Crawley, D.B., Pedersen, C.O., Lawrie, L.K., Winkelmann, F.C.: Energyplus: Energy simulation program. ASHRAE Journal 42, 49–56 (2000)Google Scholar
  4. 4.
    Danna, E., Perron, L.: Structured vs. unstructured large neighborhood search: a case study on job-shop scheduling problems with earliness and tardiness costs. In: Proc. International Conference on the Principles and Practice of Constraint Programming (CP), pp. 817–821 (2003)Google Scholar
  5. 5.
    Di Gaspero, L., Rendl, A., Urli, T.: Constraint-based approaches for balancing bike sharing systems. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 758–773. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  6. 6.
    EIA: US Department of Energy, CBECS detailed tables (2003). http://www.eia.gov/consumption/commercial/
  7. 7.
    Gouda, M., Danaher, S., Underwood, C.: Low-order model for the simulation of a building and its heating system. Building Services Engineering Research and Technology 21(3), 199–208 (2000)CrossRefGoogle Scholar
  8. 8.
    Gouda, M., Danaher, S., Underwood, C.: Building thermal model reduction using nonlinear constrained optimization. Building and Environment 37(12), 1255–1265 (2002)CrossRefGoogle Scholar
  9. 9.
    Goyal, S., Barooah, P.: A method for model-reduction of non-linear thermal dynamics of multi-zone buildings. Energy and Buildings 47, 332–340 (2012)CrossRefGoogle Scholar
  10. 10.
    Goyal, S., Ingley, H.A., Barooah, P.: Occupancy-based zone-climate control for energy-efficient buildings: Complexity vs. performance. Applied Energy 106, 209–221 (2013)CrossRefGoogle Scholar
  11. 11.
    Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 5. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Kwak, J.Y., Kar, D., Haskell, W., Varakantham, P., Tambe, M.: Building thinc: user incentivization and meeting rescheduling for energy savings. In: Proc. International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), pp. 925–932 (2014)Google Scholar
  13. 13.
    Kwak, J.y., Varakantham, P., Maheswaran, R., Chang, Y.H., Tambe, M., Becerik-Gerber, B., Wood, W.: Tesla: an energy-saving agent that leverages schedule flexibility. In: Proc. International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), pp. 965–972 (2013)Google Scholar
  14. 14.
    LeBras, R., Dilkina, B.N., Xue, Y., Gomes, C.P., McKelvey, K.S., Schwartz, M.K., Montgomery, C.A.: Robust network design for multispecies conservation. In: Proc. AAAI Conference on Artificial Intelligence (AAAI), pp. 1305–1312 (2013)Google Scholar
  15. 15.
    Lim, B.P., van den Briel, M., Thiébaux, S., Backhaus, S., Bent, R.: Hvac-aware occupancy scheduling. In: Proc. AAAI Conference on Artificial Intelligence (AAAI) (2015)Google Scholar
  16. 16.
    Majumdar, A., Albonesi, D.H., Bose, P.: Energy-aware meeting scheduling algorithms for smart buildings. In: Proc. ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys), pp. 161–168 (2012)Google Scholar
  17. 17.
    Malitsky, Y., Mehta, D., O’Sullivan, B., Simonis, H.: Tuning parameters of large neighborhood search for the machine reassignment problem. In: Gomes, C., Sellmann, M. (eds.) CPAIOR 2013. LNCS, vol. 7874, pp. 176–192. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  18. 18.
    McCormick, G.P.: Computability of global solutions to factorable nonconvex programs: Part I- convex underestimating problems. Mathematical Programming 10(1), 147–175 (1976)CrossRefMATHMathSciNetGoogle Scholar
  19. 19.
    Melbourne University: Patat dataset (2002). http://goo.gl/XtNwpR
  20. 20.
    Oldewurtel, F., Parisio, A., Jones, C.N., Gyalistras, D., Gwerder, M., Stauch, V., Lehmann, B., Morari, M.: Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings 45, 15–27 (2012)CrossRefGoogle Scholar
  21. 21.
    Pan, D., Yuan, Y., Wang, D., Xu, X., Peng, Y., Peng, X., Wan, P.J.: Thermal inertia: towards an energy conservation room management system. In: Proc. IEEE International Conference on Computer Communications (INFOCOM), pp. 2606–2610 (2012)Google Scholar
  22. 22.
    Portland State University: Efficient class scheduling conserves energy (2012). http://goo.gl/cZwgB
  23. 23.
    Rendl, A., Prandtstetter, M., Hiermann, G., Puchinger, J., Raidl, G.: Hybrid heuristics for multimodal homecare scheduling. In: Beldiceanu, N., Jussien, N., Pinson, É. (eds.) CPAIOR 2012. LNCS, vol. 7298, pp. 339–355. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. 24.
    Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, M.J., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, p. 417. Springer, Heidelberg (1998) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Boon Ping Lim
    • 1
  • Menkes van den Briel
    • 1
  • Sylvie Thiébaux
    • 1
  • Russell Bent
    • 2
  • Scott Backhaus
    • 2
  1. 1.NICTA and The Australian National UniversityCanberraAustralia
  2. 2.Los Alamos National LaboratoryLos AlamosUSA

Personalised recommendations