Smart-home electrical energy scheduling system using multi-objective antlion optimizer and evidential reasoning

Document Type : Article


1 Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of ‎Science and Technology, Narmak, Tehran, P.O. Box 16846-13114, Iran‎

2 Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Narmak, Tehran, Postal Code 16846-13114, Iran.


Smart-home energy-management-systems (SHEMSs) are widely used for energy management in smart buildings. Energy management in smart homes is an arduous task and necessitates efficient scheduling of appliances in buildings. Scheduling of smart appliances is usually enmeshed by various and sometimes contradictory criteria which should be considered concurrently in the scheduling process. Multi-criteria decision-making (MCDM) techniques are able to select the most suitable alternative among copious ones. This paper tailors a comprehensive framework which merges MCDM techniques with evolutionary multi-objective optimization (EMOO) techniques for selecting the most proper schedule for appliances by creating a trade-off between optimization criteria. A Multi-Objective Ant Lion Optimizer (MOALO) is tailored and tested on a smart home case study to detect all the Pareto solutions. A benchmark instance of the appliance scheduling is solved employing the proposed methodology, Shannon’s entropy technique is employed to find the objectives’ corresponding weights, and afterward, the acquired Pareto optimal solutions are ranked utilizing the Evidential Reasoning (ER) method. By inspecting the efficiency of every solution considering multiple criteria such as unsafety, electricity cost, delay, Peak Average Ratio, and CO2 emission, the proposed approach confirms its effectiveness in enhancing the method for smart appliance scheduling.


Main Subjects

1. Iwafune, Y. and Yagita, Y. High-resolution determinant  analysis of Japanese residential electricity  consumption using home energy management system  data", Energy Build., 116, pp. 274{284 (2016).  2. Nejat, P., Jomehzadeh, F., Taheri, M.M., et al. A  global review of energy consumption, CO2 emissions  and policy in the residential sector (with an overview of  the top ten CO2 emitting countries)", Renew. Sustain.  Energy Rev., 43, pp. 843{862 (2015).  3. Geng, Y., Chen, W., Liu, Z., et al. A bibliometric  review: Energy consumption and greenhouse gas  emissions in the residential sector", J. Clean. Prod.,  159(800), pp. 301{316 (2017).  4. Naji, H.I., Mahmood, M., and Mohammad, H.E.  Using BIM to propose building alternatives towards  lower consumption of electric power in Iraq", Asian J.  Civ. Eng., 20(5), pp. 669{679 (2019).  5. Kaveh, A., Shamsapour, N., Sheikholeslami, R., et al.  Forecasting transport energy demand in Iran using  meta-heuristic algorithms", Int. J. Optim. Civ. Eng.,  2(4), pp. 533{544 (2012).  6. Kaveh, A., Advances in Metaheuristic Algorithms for  Optimal Design of Structures, Second Edi, Springer  International Publishing, Cham, Switzerland (2017).  7. Kaveh, A. and Vazirinia, Y. Chaotic vibrating particles  system for resource constraint project scheduling  problem", Sci. Iran. Trans. A Civ. Eng. (In Press).  8. Golberg, D.E., Genetic Algorithms in Search, Optimization,  and Machine Learning, Addison-Wesley  Publishing Company, Reading, Massachusetts (1989).  9. Russell, E. and Kennedy, J. A new optimizer using  particle swarm theory", Proc. Sixth Int. Symp. Micro  Mach. Hum. Sci., Nagoya, Japan, pp. 39{43 (1995).  10. Kaveh, A. and Mahdavi, V.R. Resource allocation  and time-cost trade-o_ using colliding bodies optimization",  In Colliding Bodies Optimization: Extensions  and Applications, Springer International Publishing,  Cham, pp. 261{277 (2015).  11. Kaveh, A. and Ilchi Ghazaan, M. A new metaheuristic  algorithm: vibrating particles system", Sci.  Iran. Trans., A, Civ. Eng., 24(2), pp. 1{32 (2017).  12. Mirjalili, S. The ant lion optimizer", Adv. Eng. Softw.,  83, pp. 80{98 (2015).  13. Deb, K., Pratap, A., Agarwal, S., et al. A fast and  elitist multiobjective genetic algorithm: NSGA-II",  IEEE Trans. Evol. Comput., 6(2), pp. 182{197 (2002).  14. Coello, C.A., Pulido, G.T., and Lechuga, M.S. Handling  multiple objectives with particle swarm optimization",  IEEE Trans. Evol. Comput., 8(3), pp. 256{279  (2004).  15. Kaveh, A. and Mahdavi, V.R. Multi-objective colliding  bodies optimization algorithm for design of  trusses", J. Comput. Des. Eng., 6(1), pp. 49{59 (2019).  16. Kaveh, A. and Ilchi Ghazaan, M. A new VPS-based  algorithm for multi-objective optimization problems",  Eng. With Comput. (In press).  Kaveh, A. and Ilchi Ghazaan, M. A new VPS-based  algorithm for multi-objective optimization problems",  Eng. Comput., (0123456789) (2019).  17. Mirjalili, S., Jangir, P., and Saremi, S. Multiobjective  ant lion optimizer: a multi-objective optimization  algorithm for solving engineering problems",  Appl. Intell., 46(1), pp. 79{95 (2017).  18. Kazemzadeh Azad, S. Seeding the initial population  with feasible solutions in metaheuristic optimization of  steel trusses", Eng. Optim., 50(1), pp. 89{105 (2018).  19. Kazemzadeh Azad, S. Enhanced hybrid metaheuristic  algorithms for optimal sizing of steel truss structures  with numerous discrete variables", Struct. Multidiscip.  Optim., 55(6), pp. 2159{2180 (2017).  20. Mela, K., Tiainen, T., Heinisuo, M., et al. Comparative  study of multiple criteria decision making methods  for building design", Adv. Eng. Informatics, 26(4), pp.  716{726 (2012).  21. Monghasemi, S., Nikoo, M.R., Khaksar Fasaee, et  al. A novel multi criteria decision making model  for optimizing time-cost-quality trade-o_ problems in  construction projects", Expert Syst. Appl., 42(6), pp.  3089{3104 (2015).  22. Mela, K., Tiainen, T., and Heinisuo, M. Comparative  study of multiple criteria decision making methods for  building design", Adv. Eng. Informatics, 26(4), pp.  716{726 (2012).  23. Wallenius, J., Fishburn, P.C., Zionts, S., et al. Multiple  criteria decision making, multiattribute utility  theory: Recent accomplishments and what lies ahead",  Manage. Sci., 54(7), pp. 1336{1349 (2008).  24. Loken, E. Use of multicriteria decision analysis methods  for energy planning problems", Renew. Sustain.  Energy Rev., 11(7), pp. 1584{1595 (2007).  25. Hobbs, B.F. and Meier, P.M. Multicriteria methods  for resource planning: An experimental comparison",  IEEE Trans. Power Syst., 9(4), pp. 1811{1817 (1994).  26. Belton, V. and Stewart, T.J., Multiple Criteria Decision  Analysis, An Integrated Approach, Springer  Science & Business Media (2002).  27. Saaty, T.L. Decision making with the analytic hierarchy  process", Sci. Iran., 9(3), pp. 215{229 (2002).  28. Bazargan-Lari, M.R. An evidential reasoning approach  to optimal monitoring of drinking water distribution  systems for detecting deliberate contamination  events", J. Clean. Prod., 78, pp. 1{14 (2014).  29. Lai, Y.-J., Liu, T.-Y., and Hwang, C.-L. TOPSIS for  MODM", Eur. J. Oper. Res., 76, pp. 486{500 (1994).  30. Brans, J.P., Vincke, P., and Mareschal, B. How to  select and how to rank projects: The PROMETHEE  method", Eur. J. Oper. Res., 24, pp. 228{238 (1986).  A. Kaveh and Y. Vazirinia/Scientia Iranica, Transactions A: Civil Engineering 27 (2020) 177{201 195  31. Roy, B. The outranking approach and the foundations  of electre methods", Readings Mult. Criteria Decis.  Aid, C.A. e Costa, Ed., Springer Berlin Heidelberg,  Berlin, Heidelberg, pp. 155{183 (1990).  32. Ishizaka, A. and Nemery, P., Multi-Criteria Decision  Analysis: Methods and Software, John Wiley & Sons  (2013).  33. Yang, J.B., Wang, Y.M., Xu, D.L., et al. The  evidential reasoning approach for MADA under both  probabilistic and fuzzy uncertainties", Eur. J. Oper.  Res., 171(1), pp. 309{343 (2006).  34. Chaudhuri, S. and Deb, K. An interactive evolutionary  multi-objective optimization and decision making  procedure", Appl. Soft Comput. J., 10(2), pp. 496{511  (2010).  35. Du, Y.F., Jiang, L., Li, Y.Z., Counsell, J., et al.  Multi-objective demand side scheduling considering  the operational safety of appliances", Appl. Energy,  179, pp. 864{874 (2016).  36. Zhao, Z., Lee, W.C., Shin, Y., and Song, K.-B. An  optimal power scheduling method for demand response  in home energy management system", IEEE Trans.  Smart Grid, 4(3), pp. 1391{1400 (2013).  37. Sou, K.C.,Weimer, J., Sandberg, H., et al. Scheduling  smart home appliances using mixed integer linear  programming", Proc. IEEE Conf. Decis. Control, pp.  5144{5149 (2011).  38. Ngatchou, P., Zarei, A., and El-Sharkawi, M. Pareto  multi objective optimization", Proc. 13th Int. Conf.  Intell. Syst. Appl. to Power Syst., pp. 84{91 (2005).  39. Pareto, V. Cours d'economie politique", Libr. Droz,  25 (1964).  40. Shannon, C.E. A mathematical theory of communication",  Bell Syst. Tech. J., 27(3), pp. 379{423 (1948).  41. Wang, T., Liu, J., Li, J., et al. An intuitionistic  fuzzy OWA-TOPSIS method for collaborative network  formation considering matching characteristics", Sci.  Iran., 25(3), pp. 1671{1687 (2018).  42. Wang, Y.J. A fuzzy multi-criteria decision-making  model based on simple additive weighting method and  relative preference relation", Appl. Soft Comput. J.,  30, pp. 412{420 (2015).  43. Deng, H., Yeh, C.H., and Willis, R.J. Inter-company  comparison using modi_ed TOPSIS with objective  weights", Comput. Oper. Res., 27(10), pp. 963{973  (2000).  44. Keeney, R.L. and Rai_a, H., Decisions with Multiple  Objectives: Preferences and Value Trade-O_s, Cambridge  University Press (1993).  45. Guitouni, A., Martel, J., and Vincke, P., A framework  to Choose a Discrete Multicriterion Aggregation  Procedure, Defence Research Establishment Valcatier  (1999).  46. Xu, D.L. An introduction and survey of the evidential  reasoning approach for multiple criteria decision analysis",  Ann. Oper. Res., 195(1), pp. 163{187 (2012).  47. Ameren Illinois Power Company, Real-time pricing for  residential customers, https://www. powersmartpricing.  org/prices/.  48. Kristinsd_ottir, A.R., Stoll, P., Nilsson, A., and Brandt,  N. Description of climate impact calculation methods  of the CO2e-signal for the active house project", KTH  Royal Institute of Technology (2013).  49. Taguchi, G., Introduction to Quality Engineering: Designing  Quality into Products and Processes, Asian  Productivity Organization (1986).  50. Jolai, F., Ase_, H., Rabiee, M., et al. Bi-objective  simulated annealing approaches for no-wait two-stage  exible ow shop scheduling problem", Sci. Iran.,  20(3), pp. 861{872 (2013).