Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31743
Centralized Peak Consumption Smoothing Revisited for Habitat Energy Scheduling

Authors: M. Benbouzid, Q. Bresson, A. Duclos, K. Longo, Q. Morel


Currently, electricity suppliers must predict the consumption of their customers in order to deduce the power they need to produce. It is then important in a first step to optimize household consumptions to obtain more constant curves by limiting peaks in energy consumption. Here centralized real time scheduling is proposed to manage the equipments starting in parallel. The aim is not to exceed a certain limit while optimizing the power consumption across a habitat. The Raspberry Pi is used as a box; this scheduler interacts with the various sensors in 6LoWPAN. At the scale of a single dwelling, household consumption decreases, particularly at times corresponding to the peaks. However, it would be wiser to consider the use of a residential complex so that the result would be more significant. So the ceiling would no longer be fixed. The scheduling would be done on two scales, on the one hand per dwelling, and secondly, at the level of a residential complex.

Keywords: Smart grid, Energy box, Scheduling, Gang Model, Energy consumption, Energy management system, and Wireless Sensor Network.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1388


[1] N. Shaikh-Husin, M.K. Hani, Teoh Giap Seng: Implementation of Recurrent Neural Network Algorithm for Shortest Path Calculation in Network Routing, Proc. Intern. Symp. on Parallel Architectures Algorithms and Networks (I-SPAN '02), pp. 313-317, 22-24 May 2002.
[2] Liu Rong, Liu Ze-Min, Zhou Zheng : Neural Network Approach for Communication Network Routing Problem, Proc. Computer, Communication, Control and Power Engineering (TENCON '93), pp.649– 652, Vol.3, Oct.19-21, 1993.
[3] St. Russell, P. Norvig: Artificial Intelligence: A Modern Approach, Prentice-Hall, Upper Saddle River, New Jersey, 2003.
[4] L. Padgham, M. Winikoff: Developing Intelligent Agent Systems: A Practical Guide, Wiley, New York, 2004.
[5] A. Konar: Artificial Intelligence and Soft Computing, CRC Press, New York, 1999.
[6] M. Hutter: Universal Algorithmic Intelligence: A Mathematical Top->Down Approach, Artificial General Intelligence, pp.227-290, Springer, 2007.
[7] A.D. Linkevitch: Self Organization in Intelligent Multi-Agent Systems and Neural Networks, Nonlinear Phenomena in Complex Systems, Part I: Vol.4 (1), pp.18-46, Part II: Vol.4 (3), pp.212-249.
[8] K. Fischer: Holonic Multi-Agent Systems – Theory and Applications, Proc. 9th Portuguese Conference on Progress in Artificial Intelligence (EPIA-99), LNAI Vol.1695, LNAI. Springer Verlag, 1999.
[9] S. Rodriguez, V. Hilaire, A. Koukam: Formal Specification of Holonic Multi-Agent System Framework, Proc. Intern. Conf. on Computational Science, Lecture Notes in Computer Science, Vol.3516, pages 719–726. Springer-Verlag, 2005.
[10] E. Bonabeau, M. Dorigo, G. Theraulaz: Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, Oxford, 1999.
[11] D. Martens, M. De Backer, R. Haesen, J. Vanthienen, M. Snoeck, B. Baesens : Classification with Ant Colony Optimization, IEEE Trans. on Evolutionary Computation, Vol.11(5), pp. 651-665, 2007.
[12] M. Zlochin, M. Birattari, N. Meuleau, M. Dorigo: Model-based search for combinatorial optimization: A critical survey, Annals of Operations Research, Vol.131, pp.373-395, 2004.
[13] J. Ferber: Les Systèmes Multi-Agents, Versune Intelligence Collective, Inter Editions, Paris, 1995.
[14] N. Vlassis: A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence, Morgan and Claypool Synthesis Lectures on Artificial Intelligence and Machine Learning, Vol.2, 2007.
[15] Jade Software at, Janus Software at
[16] P. Palensky, D. Dietrich: Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads, IEEE Trans. Indus. Informatics, Vol.7 (3), pp.381-388, 2011.
[17] F. Saffre, R. Gedge: Demand-Side Management for the Smart Grid, in Proc. IEEE/IFIP Network Oper. Manage. Symp. Workshops (NOMSWksps), Apr. 2010, pp 300-303.
[18] J. Goossens, V. Berten: Gang FTP Scheduling for Periodic and Parallel Rigid Real Time Tasks, RTNS 2010, ULB, Brussels.
[19] J. Goossens, P. Courbin, V. Berten :Gang Fixed Priority Scheduling of Periodic Moldable Real-Time Tasks, Proc. JRWRTC 2011, Nantes, Sept. 29-30, 2011.
[20] Cabinet O. Sidler: Analyse et Valorisationdes Campagnes de Mesure sur les Usages Electriques dans le Secteur Résidentiel Français.
[21] Atmel Store/Atmel AVRRAVEN at