Abstract
One of the problems we encounter when dealing with the optimization of household energy consumption is how to reduce the consumption of air conditioning systems without reducing the comfort level of the residents. The systems that have been proposed so far do not succeed at optimizing the electricity consumed by heating and air conditioning systems because they do not monitor all the variables involved in this process, often leaving users’ comfort aside. It is therefore necessary to develop a solution that monitors the factors which contribute to greater energy consumption. Such a solution must have a self-adaptive architecture with the capacity of self-organization which will allow it to adapt to changes in user temperature preferences. The methodology that is the most suitable for the development of such solution are virtual agent organizations, they allow for the management of wireless sensor networks (WSN) and the use of Case-Based Reasoning (CBR) for predicting the presence of people at home. This work presents an energy optimization system based on virtual agent organizations (VO-MAS) that obtains the characteristics of the environment through sensors and user behavior pattern using a CBR system. A case study was carried out in order to evaluate the performance of the proposed system, the results show that 22.8% energy savings were achieved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems–a review of model predictive control (MPC). Build. Environ. 72, 343–355 (2014)
Al-Daraiseh, A., El-Qawasmeh, E., Shah, N.: Multi-agent system for energy consumption optimisation in higher education institutions. J. Comput. Syst. Sci. 81(6), 958–965 (2015)
Annunziato, M., Pierucci, P.: The emergence of social learning in artificial societies. Appl. Evol. Comput. 293–294 (2003)
Bogdan, M.: How to use the DHT22 sensor for measuring temperature and humidity with the arduino board. ACTA Universitatis Cibiniensis 68(1), 22–25 (2016)
Briones, A.G., González, J.R., de Paz Santana, J.F.: A drug identification system for intoxicated drivers based on a systematic review. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 4(4), 83–101 (2015)
Briones, A.G., González, J.R., de Paz Santana, J.F., Rodríguez, J.M.C.: Obtaining relevant genes by analysis of expression arrays with a multi-agent system. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 3(3), 35–42 (2014)
Cai, J., Kim, D., Putta, V.K., Braun, J.E., Hu, J.: Multi-agent control for centralized air conditioning systems serving multi-zone buildings. In: American Control Conference (ACC), pp. 986–993. IEEE (2015)
Casals, X.G.: Analysis of building energy regulation and certification in Europe: their role, limitations and differences. Energy Build. 38(5), 381–392 (2006)
Clausen, A., Demazeau, Y., Jørgensen, B.N.: Load management through agent based coordination of flexible electricity consumers. In: Demazeau, Y., Decker, K.S., Bajo Pérez, J. (eds.) PAAMS 2015. LNCS (LNAI), vol. 9086, pp. 27–39. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18944-4_3
Clausen, A., Umair, A., Demazeau, Y., Jørgensen, B.N.: Agent-based integration of complex and heterogeneous distributed energy resources in virtual power plants. In: Demazeau, Y., Davidsson, P., Bajo, J. (eds.) PAAMS 2017. LNCS (LNAI), vol. 10349, pp. 43–55. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59930-4_4
Corchado, J.M., Laza, R.: Constructing deliberative agents with case-based reasoning technology. Int. J. Intell. Syst. 18(12), 1227–1241 (2003)
Dodier, R.H., Henze, G.P., Tiller, D.K., Guo, X.: Building occupancy detection through sensor belief networks. Energy Build. 38(9), 1033–1043 (2006)
Dong, B., Lam, K.P.: Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network. J. Build. Perform. Simul. 4(4), 359–369 (2011)
FacilitiesNet: Building Owners & Facility Executives - Building Operating Management - Facilities Management Magazine (2011). http://www.facilitiesnet.com/bom/. Accessed 12 Jan 2018
Faia, R., Pinto, T., Abrishambaf, O., Fernandes, F., Vale, Z., Corchado, J.M.: Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management. Energy Build. 155, 269–281 (2017)
González Briones, A., Chamoso, P., Barriuso, A.L.: Review of the main security problems with multi-agent systems used in E-commerce applications (2016)
Guerrisi, A., Martino, M., Tartaglia, M.: Energy saving in social housing: an innovative ICT service to improve the occupant behaviour. In: International Conference on Renewable Energy Research and Applications (ICRERA), pp. 1–6. IEEE (2012)
Harris, C., Cahill, V.: Exploiting user behaviour for context-aware power management. In: IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2005), vol. 4, pp. 122–130. IEEE (2005)
Herkel, S., Knapp, U., Pfafferott, J.: A preliminary model of user behaviour regarding the manual control of windows in office buildings. In: Proceedings of the 9th International IBPSA Conference BS 2005, Montréal, pp. 403–410 (2005)
Hoes, P., Hensen, J.L.M., Loomans, M.G.L.C., De Vries, B., Bourgeois, D.: User behavior in whole building simulation. Energy Build. 41(3), 295–302 (2009)
Li, Y.C., Hong, S.H.: BACnet–EnOcean smart grid gateway and its application to demand response in buildings. Energy Build. 78, 183–191 (2014)
McArthur, S.D.J., Davidson, E.M., Catterson, V.M., Dimeas, A.L., Hatziargyriou, N.D., Ponci, F., Funabashi, T.: Multi-agent systems. IEEE Trans. Power Syst. 22(4), 1753–1759 (2007)
Moghavvemi, M., Seng, L.C.: Pyroelectric infrared sensor for intruder detection. In: TENCON 2004. 2004 IEEE Region 10 Conference, vol. 500, pp. 656–659. IEEE (2004)
Mozer, M.C.: The neural network house: an environment hat adapts to its inhabitants. In: Proceedings of the AAAI Spring Symposium on Intelligent Environments, vol. 58 (1998)
Ramos, J., Castellanos-Garzón, J.A., González-Briones, A., de Paz, J.F., Corchado, J.M.: An agent-based clustering approach for gene selection in gene expression microarray. Interdiscip. Sci. Comput. Life Sci. 9(1), 1–13 (2017)
Recio-García, J.A., González-Calero, P.A., Díaz-Agudo, B.: jcolibri2: a framework for building case-based reasoning systems. Sci. Comput. Program. 79, 126–145 (2014)
Samad, T., Frank, B.: Leveraging the web: a universal framework for building automation. In: American Control Conference, ACC 2007, pp. 4382–4387. IEEE (2007)
Wang, Z., Yang, R., Wang, L.: Multi-agent control system with intelligent optimization for smart and energy-efficient buildings. In: IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society, pp. 1144–1149. IEEE (2010)
Zato, C., Villarrubia, G., Sánchez, A., Bajo, J., Corchado, J.M.: PANGEA: a new platform for developing virtual organizations of agents. Int. J. Artif. Intell. 11(A13), 93–102 (2013)
González-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., Corchado, J.M.: Energy optimization using a case-based reasoning strategy. Sensors 18(3), 865 (2018)
González-Briones, A., Chamoso, P., Yoe, H., Corchado, J.M.: GreenVMAS: virtual organization based platform for heating greenhouses using waste energy from power plants. Sensors 18(3), 861 (2018)
Acknowledgements
This research has been partially supported by the European Regional Development Fund (FEDER) within the framework of the Interreg program V-A Spain-Portugal 2014-2020 (PocTep) under the IOTEC project grant 0123_IOTEC_3_E and by the Spanish Ministry of Economy, Industry and Competitiveness and the European Social Fund under the ECOCASA project grant RTC-2016-5250-6. The research of Alfonso González-Briones has been co-financed by the European Social Fund (Operational Programme 2014-2020 for Castilla y León, EDU/310/2015 BOCYL).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
González-Briones, A., Prieto, J., Corchado, J.M., Demazeau, Y. (2018). EnerVMAS: Virtual Agent Organizations to Optimize Energy Consumption Using Intelligent Temperature Calibration. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-92639-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92638-4
Online ISBN: 978-3-319-92639-1
eBook Packages: Computer ScienceComputer Science (R0)