ANN-GA smart appliance scheduling for optimised energy management in the domestic sector

Energy and Buildings

Volumn 111, Issue , 2016, Pages 311-325

  Yuce, Baris ^a   Rezgui, Yacine ^a   Mourshed, Monjur ^a  

^a CARDIFF UNIVERSITY   (United Kingdom)

Author keywords

ANN; Domestic building; Energy management; Optimisation, Genetic Algorithm, Scheduling; Parameter tuning; PMV

Indexed keywords

ARTIFICIAL INTELLIGENCE; ELECTRIC POWER TRANSMISSION NETWORKS; ENERGY MANAGEMENT; GENETIC ALGORITHMS; NEURAL NETWORKS; OPTIMIZATION; SCHEDULING; SCHEDULING ALGORITHMS; WIND TURBINES;

ANN; DOMESTIC BUILDINGS; OPTIMISATIONS; PARAMETER-TUNING; PMV;

DOMESTIC APPLIANCES;

EID: 84949267100     PISSN: 03787788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enbuild.2015.11.017     Document Type: Article

References (30)

1. E.C. Challenging and Changing Europe's Built Environment: A Vision for a Sustainable and Competitive Construction Sector by 2030 2005 European Construction Technology Platform (ECTP) Brussels
2. BERR Digest of UK Energy Statistics 2007 Department for Business Enterprise and Regulatory Reform London (http://stats.berr.gov.uk/energystats/dukes07-c6.pdf) (accessed 24.09.14)
3. J.A. Clarke, C.M. Johnstone, N.J. Kelly, P.A. Strachan, and P. Tuohy The role of built environment energy efficiency in a sustainable UK energy economy Energy Policy 36 2008 4605 4609
4. EUROSTAT Energy: Yearly Statistics 2008 2010 Publications Office of the European Union Luxembourg 2010
5. E.C. Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the Energy Performance of Buildings Off. J. Eur. Commun. 2010 65 71
6. S. Ramchurn, P. Vytelingum, A. Rogers, and N. Jennings Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence Commun. ACM 55 4 2012 86 97
7. A.N. Lund, P.A. Andersen, B.V. Ostergaard, and D. Mathiesen Connolly From electricity to smart energy systems - a market operation based approach and understanding Energy 42 2012 96 102
8. M. Yilmaz, and P.T. Krein Review of the Impact of vehicle-to-grid technologies on distribution systems and utility interfaces IEEE Trans. Power Electron. 28 2013 5673 5689 10.1109/TPEL. 2012.22275007
9. M.F. Haniff, H. Selamat, R. Yusof, S. Buyamin, and F.S. Ismail Review of HVAC scheduling techniques for building towards energy-efficient cost-effective operations Renew. Sustain. Energy Rev. 27 2013 94 103 10.1016/j.rser.2013.06.041
10. D. Setlhaolo, X. Xia, and J. Zhang Optimal scheduling of household appliances for demand response Electric Power Systems Res. 116 2014 24 28 10.1016/j.epsr.2014.04.012
11. A. Majumdar, D.H. Albonesi, P. Bose, Energy - aware meeting scheduling algorithms for smart buildings, BuildSys '12, Proceedings of the Fourth Workshop on Embedding Sensing Systems for Energy-Efficiency in Buildings, 2012, pp. 161-168. DOI 10.1145/2422531.2422560.
12. I. Petri, H. Li, Y. Rezgui, C. Yang, B. Yuce, and B. Jayan A modular optimisation model for reducing energy consumption in large scale building facilities Renew. Sustain. Energy Rev. 38 2014 990 1002 doi:10.1016.j.rser.2014.07.044
13. B. Yuce, H. Li, Y. Rezgui, I. Petri, B. Jayan, and C. Yang Utilizing artificial neural network to predict energy consumption and thermal comfort levels: An indoor swimming pool case study Energy Build. 80 2014 45 46 10.1016/j.enbuild.2014.04.052
14. S.J. Kang, J. Park, K.Y. Oh, J.G. Noh, and H. Park Scheduling-based rea time energy flow control strategy for building energy management system Energy Build. 75 2014 239 248
15. Solvers, 2014, Mathematical programming solvers (http://www.aimms.com/aimms/solvers/conopt/) (accessed 24.09.14).
16. J.K. Gruber, S. Jahromizadeh, M. Prodanovic, and V. Rakocevic Application-oriented modelling of domestic energy demand Int. J. Electric. Power Energy Systems 61 2014 656 664 10.1016/j.ijepes.2014.04.008
17. P.M. Ferreira, A.E. Ruano, S.M. Silva, and E.Z.E. Conceicao Neural networks based predictive control for thermal comfort and energy savings in public buildings Energy Build. 55 2012 238 251
18. N. Morel, M. Bauer, M. El-Khoury, and J. Krauss Neurobat, a predictive and adaptive heating control system using artificial neural network Int. J. Solar Energy 21 2001 161 201
19. S. Atthajariyakul, and T. Leephakpreeda Neural computing thermal comfort index for HVAC systems Energy Convers. Manag. 46 15-16 2005 2553 2565
20. P.M. Ferreira, S.M. Silva, A.E. Ruano, A.T. Negrier, E.Z.E. Conceicao, Neural network PMV estimation for model-based predictive control HVAC systems. In WCCI 2012 IEEE world congress on computational intelligence, Brisbane, Australia, June 10-15, 2012.
21. B. Yuce Novel Computational Technique for Determining Depth Using the Bees Algorithm and Blind Image Deconvolution (Ph.D. thesis) 2012 Cardiff University UK
22. M.S. Yuce, E. Packianather, D.T. Mastrocinque, A. Pham, and Lambiase Honey bees inspired optimization method: the Bees Algorithm Insects 44 4 2013 646 662 doi:2075-445010.3390/insects4040646
23. C. Yang, H. Li, Y. Rezgui, I. Petri, B. Yuce, B. Chen, and B. Jayan High throughput computing based distributed genetic algorithm for building energy consumption optimization Energy Build. 76 2013 92 101 10.1016/j.enbuild.2014.02.053
24. DesignBuilder, DesignBuilder - building simulation made easy, 2014 (http://www.designbuilder.co.uk/content/view/43/64/) (accessed 01.09.14).
25. EnergyPlus, EnergyPlus Energy Simulation Software, 2014 (http://apps1.eere.energy.gov/buildings/energyplus/) (accessed 01.09.14).
26. T.H. Hou, C.H. Su, and W.L. Liu Parameters optimization of a nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm Powder Technol. 173 2006 153 162
27. G.W. Delius, Table of Taguchi designs (orthogonal arrays), 2014 (http://www.york.ac.uk/depts/maths/tables/taguchi-table.htm/) (accessed 01.09.14).
28. E. Yuce, M.S. Mastrocinque, D.T. Packianather, A. Pham, F. Lambiase, and F. Fruggiero, 2014, Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects, Production and Manufacturing Research, 2(1), 291-308
29. I. Petri, H. Li, Y. Rezgui, C. Yang, B. Yuce, and B. Jayan A HPC based cloud model for real-time energy optimisation Enterp. Inf. Systems 1-21 2014 2169 3277 10.1080/21693277.2014.892442
30. M. Dibley, H. Li, Y. Rezgui, and J. Miles An ontology framework for intelligent sensor-based building monitoring Autom. Constr. 28 2012 1 14