A Relevant Fuzzy Logic Algorithm to Better Optimize Electricity Consumption in Individual Housing S. Bissey, S. Jacques and J.-C. Le Bunetel 2017/04/25

Abstract

In this article, a Fuzzy Logic algorithm (MATLAB environment) is descripted to better predict and
manage electricity consumption in individual housing. Several measurements were performed in 3 houses to have an idea of typical electrical energy consumptions. Without any prediction model, the simulation results of the management system exhibit that it is not possible to smooth all peak demands. In particular, this smoothing is in chronological sequence. Using the prediction and management modeling, the highest peak demands can be forecasted. As a consequence, if
the house is composed of a storage system, all stored electricity can be reinjected during the highest peak periods. Finally, the system proposed here provides safety guarantees, and particularly during AC-line disconnection.

Authors and affiliations

S. Bissey, S. Jacques and J.-C. Le Bunetel
University of Tours, GREMAN CNRS UMR 7347, Tours (France)

Key word

Demand side management, load forecasting, smart grid, electricity consumption.

References

[1] IEA Statistics © OECD/IEA 2014. Electric power consumption (kWh per capita) [Internet]. [cited 2016 October 15]. Retrieved from http://data.worldbank.org/indicator/EG.USE .ELEC.KH.PC.
[2] IEA Statistics © OECD/IEA 2014. Electric power consumption (kWh per capita) [Internet]. [cited 2016 October 15]. Retrieved from http://data.worldbank.org/indicator/EG.USE .ELEC.KH.PC?locations=FR.
[3] Swan, L. G., & Ugursal, V. I. (2009). Modeling of end-use energy consumption in the residential sector: A review of
modeling techniques. Renewable and Sustainable Energy Reviews, 13(8), 1819–1835.
[4] Grandjean, A., Adnot, J., & Binet, G. (2012). A review and an analysis of the residential electric load curve models.
Renewable and Sustainable Energy Reviews, 16(9), 6539–6565.
[5] Tascikaraoglu, A., Boynuegri, A. R., & Uzunoglu, M. (2014). A demand side management strategy based on forecasting of residential renewable sources: A smart home system in Turkey. Energy and Buildings, 80, 309–320.
[6] Mohsenian-Rad, A.-H., Wong, V. W. S., Jatskevich, J., Schober, R., & Leon-Garcia, A. (2010). Autonomous
Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid.
IEEE Transactions on Smart Grid, 1(3), 320–331.
[7] Pappas, S. S., Ekonomou, L., Karamousantas, D. C., Chatzarakis, G. E., Katsikas, S. K., & Liatsis, P. (2008).
Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models. Energy, 33(9), 1353–
1360.
[8] Jovanoviæ, R. Ž., Sretenoviæ, A. A., & Živkoviæ, B. D. (2015). Ensemble of various neural networks for prediction of
heating energy consumption. Energy and Buildings, 94, 189–199.
[9] Barak, S., & Sadegh, S. S. (2016). Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid
algorithm. International Journal of Electrical Power & Energy Systems, 82, 92–104.
[10] Chenthur Pandian, S., Duraiswamy, K., Christober Asir Rajan, C., & Kanagaraj, N. (2006). Fuzzy approach for short
term load forecasting. Electric Power Systems Research, 76(6–7), 541–548.
[11] Setlhaolo, D., & Xia, X. (2016). Combined residential demand side management strategies with coordination and
economic analysis. International Journal of Electrical Power & Energy Systems, 79, 150–160.
[12] Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting: a review
and evaluation. IEEE Transactions on Power Systems, 16(1), 44–55.