An Agent Based Fuzzy Control for Smart Home Energy Management in Smart Grid Environment

asma garrab, adel bouallegue, ridha bouallegue

Abstract


Energy management in Smart Home environment is one of the main topics adopted in Smart Grid research field. In this paper, we present a Multi-Agent System (MAS) for a Smart Home intelligent control. Such a solution was integrated in a smart meter in order to alter the shape of the residential load curve. The MAS is strong appropriate to solve complex distributed problems as home automation system. Our contribution consists in performing an algorithm for scheduling appliances tasks, and designing a model for a direct load control which may accommodate customer preferences. The direct load control is based on Fuzzy Logic Control (FLC) using new fuzzy power indicator. In order to successfully implement our solution, customer acceptance of the direct load control is vital. We aim to reach a compromise among habitant comfort and electric bills in addition of satisfying technological constraints of appliances. Simulation results have proved the effectiveness of the proposed solution in energy savings.


Total Views: 149

Keywords


Energy Management; Fuzzy Logic Controller; Multi-Agent System; Smart Home Automation; Smart Grid; Smart Meter.

Full Text:

PDF

References


T. Ozden, and H. I. Okumus, “Designing A Load Agent For Power Management With A Multi-Agent Home Automation System”, International Symposium on Innovations in Intelligent Systems and Applications, pp. 1-5, July 2012.

X. Xue, S. Weng, C. Yan, and B. Cui, “A fast chiller power demand response control strategy for buildings connected to smart grid”, Applied Energy, vol. 137, pp. 77-87, January 2015.

M. A. Khan, N. Javaid, M. Arif, S. Saud, U. Quasim, and Z. A. Khan, “Peak load scheduling in Smart Grid communication environement”, 28th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 1025-1032, May 2014.

M. A. Ul Haq, M. Y. Hassan, H. Abdullah, H. A. Rahman, Md. P. Abdullah, F. Hussin, and D. M. Said, “A review on lighting control technologies in commercial buildings, their performance and affecting factors”, Renewable and Sustainable Energy Reviews, vol. 33, pp. 268-279, May 2014.

T. A. Nguyen, and M. Aiello, “Energy intelligent buildings based on user activity: A survey”, Energy and Buildings, vol. 56, pp. 244-257, January 2013.

M.H. Nehrir, and B.J. LaMeres, “A multiple-block fuzzy logic-based electric water heater demand-side management strategy for leveling distribution feeder demand profile”, Electric Power Systems Research, vol. 56, pp. 225-230, March 2000.

A. Garrab, A. Bouallegue, and F. B. Abdallah, “A new AMR Approach for Energy Saving in Smart Grids using Smart Meter and Practical Power Line Communication”, IEEE First International Conference on Renewable Energies and Vehicular Technology (REVET), pp. 263-269, March 2012.

S. Kaufmann, K. Künzel, and M. Loock, “Customer value of smart metering: Explorative evidence from a choice-based conjoint study in Switzerland”, Energy Policy, vol. 53, pp. 229-239, November 2012.

A.H. Mohsenian-Rad and A. Leon-Garcia. “Optimal residential load control with price prediction in real time electricity pricing environments”, IEEE Transactions on Smart Grid, vol. 1, pp. 120-133, September 2010.

T. Logenthiran, and D. Srinivasan, “Multi-agent system for the operation of an integrated microgrid”, AIP, Journal of Renewable and Sustainable Energy 4, February 2012.

M. Guerbaoui, A. ED-Dahhak, Y. ElAfou, A. Lachhab, A. Belkoura, and B. Bouchikhi, “Implementation of Direct Fuzzy Controller in Greenhouse Based on Labview”, International Journal of Electrical and Electronics Engineering Studies, vol. 1, pp. 1-13, September 2013.

A. Garrab, A. Bouallegue, and R. Bouallegue, “Multi-Agent Modeling of a Meters Network Used in Smart Grid”, World Congress on Computer Applications and Information Systems, Hammamet, Tunisia, pp. 1-5, January 17-19, 2014.

J. S. Vardakas, N. Zorba, and C. V. Verikoukis, “Scheduling policies for two-state smart home appliances in dynamic electricity pricing environments”, Energy, vol. 69, pp. 455-469, May 2014.

J. L. Rojas-Renteria, G. Macias-Bobadilla1, R. Luna-Rubio, C. A. Gonzalez-Gutierrez, A. Rojas-Molina, and J. L. Gonzalez-Perez, “Control response of electric demand by means of fuzzy logic using programmable logic controller (PLC)”, International Journal of Physical Science, pp. 1058-1067, May 2013.

H. Joumaa, S. Ploix, S. Abras, S. Pesty, and G. D. Oliveira, “A MAS integrated into Home Automation System, for the resolution of power management Problems in Smart Homes”, Energy Procedia, vol. 6, pp.786-794, March 2011.

T. Labeodan, K. Aduda, G. Boxem, and W. Zeiler, “On the application of multi-agent systems in buildings for improved building operations, performance and smart grid interaction – A survey”, Renewable and Sustainable Energy Reviews, vol. 50, pp. 1405-1414, October 2015.

S. Abras, S. Pesty, S. Ploix, M. Jacomino, “Advantages of MAS for the resolution of a power management problem in smart homes”, Advances in Practical Applications of Agents and Multiagent Systems, vol. 70, pp. 269-278, April 2010.

R. Roche , Agent-based architectures and algorithms for energy management in smart grids: Application to smart power generation and residential demand response, Ph.D. dissertation, Dept. Elect., Belfort Univ., France, 2012.

P. H. Shaikh, N. B. Mohd Nor, P. Nallagownden, and I. Ellamvazuthi, “Building Energy Management through a Distributed Fuzzy Inference System”, International Journal of Engineering and Technology (IJET), vol. 5, pp. 3236-3242, September 2013.

I. Tomicic, and M. Schatten, “Towards an Agent Based framework for Modeling Smart Self-Sustainable Systems”, Interdisciplinary Description of Complex Systems, vol. 13, pp.57-70, January 2015.

The Mathworks Inc., (2016) Fuzzy Logic Toolbox, [Online]. Available: http://www.mathworks.com/.

M. Ali, J. Jokisalo, K. Siren, and M. Lehtonen, “Combining the Demand Response of direct electric space heating and partial thermal storage using LP optimization”, Electric Power Systems Research, vol. 106, pp. 160-167, January 2014.

A. I. Dounis, and C. Caraiscos, “Advanced control systems engineering for energy and comfort management in a building environment—a review”, Renewable and Sustainable Energy Reviews, vol. 13, pp. 1246-1261, September 2009.

G. P. J. Verbong, S. Beemsterboer, and F. Sengers, “Smart Grids or Smart users? involving users in developing a low carbon electricity economy”, Energy Policy, vol. 52, pp. 117-125,May 2012.

I. Ullah, N. Javaid, Z. A. Khan, U. Qasim, Z. A. Khan, and S. A. Mehmood, “An incentive-based optimal energy consumption scheduling algorithm for residential users”, Proceedia Computer Science, vol. 52, pp.851-857, December 2015.

G. Graditi, et al., “Innovative control logics for a rational utilization of electric loads and air-conditioning system in a residential building”, Energy and Buildings, vol. 102, pp. 1-17, September 2015.

P. H. Shaikh, N. B. Mohd Nor, P. Nallagownden, I. Elamvazuthi, and T. Ibrahim, “A review on optimized control systems for buildings energy and comfort management of smart sustainable buildings”, Renewable and Sustainable Energy Reviews, vol. 34, pp. 409-429, March 2014.

A. Garrab, A. Bouallegue, and R. Bouallegue, “MAS using Fuzzy Control Technic in Eco-Building”, The 7th Inernational Coference Renewable Energy Congress (IREC2016), Hammamet, Tunisia, March 2016.

M. Diagones, L. Coby, S. Vicktoreya, and H. Ronald, “Model predictive and genetic algorithm-based optimization of residential temperature control in the presence of time-varying electricity prices”, IEEE Transactions on Industry Applications, vol. 49, pp. 1137-1145, May 2013.

R. Yang, and L. Wang, “Multi-zone building energy management using intelligent control and optimization”, Sustainable Cities and Society, vol. 6, pp. 16-21, February 2013.

P. M. Ferreira, A. E. Ruano, S. Silva, and E. Z. E. Conceiçao, “Neural networks based predictive control for thermal comfort and energy savings in public buildings”, Energy and Buildings, vol. 55, pp. 238-251, December 2012.

S. Russel, and P. Norveg, “Logical Agents”, Artificial Inteligence: A Modern Approach, Chapter. 7, 2013.

J. Liu, W. Zhang, X. Chu, and Y. Liu, “Fuzzy Logic Controller for Energy Savings in a Smart LED Lighting System Considering Lighting Comfort and Daylight”, Energy and Buildings, vol. 127, pp.95-104, September 2016.

B. Hamed, and F. Alami, “Adaptive hearchical fuzzy controller for HVAC systems in low energy buildings”, Academic Platform Journal of Engineering and Science (APJES), pp.1-7, 2015.

A. Preglej, J. Rehrl, D. Schwingshackl. I. Steiner, and M. Horn, “Energy-efficient fuzzy model-based multivariable predictive controlof a HVAC system”, Energy and Buildings, vol. 82, pp. 520-533, August 2014.

M. Killian, B. Mayer, and M. Kozek, “Cooperative fuzzy model predictive control for heating and coolings of buildings”, Energy and Buildings, vol. 112, pp. 130-140, August 2014.

P. Srikantha, C. Rosenberg, and S. Keshav, “An analysis of peak demand reductions due to elasticity of domestic appliances”, In Proceedings of the Third International IEEE/ACM Conference on Future Energy Systems (e-Energy), Madrid, Spain, May 2012.


Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

www.ijrer.org

ijrereditor@gmail.com; ilhcol@gmail.com;

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Thomson Reuters)