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Demand side management using artificial neural networks in a smart grid environment

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  • Macedo, M.N.Q.
  • Galo, J.J.M.
  • de Almeida, L.A.L.
  • de C. Lima, A.C.

Abstract

Smart grid deployment is a global trend, creating endless possibilities for the use of data generated by dynamic networks. The challenge is the transformation of this large volume of data into useful information for the electrical system. An example of this is the application of demand side management (DSM) techniques for the optimisation of power system management in real time. This article discusses the use of DSM in this new environment of electrical system and it presents a simulation that uses data acquired from digital meters, it creates patterns of load curves, uses these patterns load data to train and validate a ANN and uses this ANN to classify new data using these defined patters. The results obtained in this study show that the intelligent network environment facilitates the implementation of DSM and the use of ANN presented a satisfactory performance for the classification of load curves.

Suggested Citation

  • Macedo, M.N.Q. & Galo, J.J.M. & de Almeida, L.A.L. & de C. Lima, A.C., 2015. "Demand side management using artificial neural networks in a smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 128-133.
    • Handle: RePEc:eee:rensus:v:41:y:2015:i:c:p:128-133
    DOI: 10.1016/j.rser.2014.08.035
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    1. Ellegård, Kajsa & Palm, Jenny, 2011. "Visualizing energy consumption activities as a tool for making everyday life more sustainable," Applied Energy, Elsevier, vol. 88(5), pages 1920-1926, May.
    2. Safa, M. & Samarasinghe, S., 2011. "Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”," Energy, Elsevier, vol. 36(8), pages 5140-5147.
    3. Bradley, Peter & Leach, Matthew & Torriti, Jacopo, 2013. "A review of the costs and benefits of demand response for electricity in the UK," Energy Policy, Elsevier, vol. 52(C), pages 312-327.
    4. Javeed Nizami, SSAK & Al-Garni, Ahmed Z, 1995. "Forecasting electric energy consumption using neural networks," Energy Policy, Elsevier, vol. 23(12), pages 1097-1104, December.
    5. Faruqui, Ahmad & Sergici, Sanem & Sharif, Ahmed, 2010. "The impact of informational feedback on energy consumption—A survey of the experimental evidence," Energy, Elsevier, vol. 35(4), pages 1598-1608.
    6. Torriti, Jacopo, 2012. "Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy," Energy, Elsevier, vol. 44(1), pages 576-583.
    7. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    8. Wu, Yun-na & Chen, Jian & Liu, Li-rong, 2011. "Construction of China's smart grid information system analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4236-4241.
    9. Torriti, Jacopo & Hassan, Mohamed G. & Leach, Matthew, 2010. "Demand response experience in Europe: Policies, programmes and implementation," Energy, Elsevier, vol. 35(4), pages 1575-1583.
    10. Wissner, Matthias, 2011. "The Smart Grid - A saucerful of secrets?," Applied Energy, Elsevier, vol. 88(7), pages 2509-2518, July.
    11. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
    12. Prüggler, Natalie & Prüggler, Wolfgang & Wirl, Franz, 2011. "Storage and Demand Side Management as power generator’s strategic instruments to influence demand and prices," Energy, Elsevier, vol. 36(11), pages 6308-6317.
    13. Alagoz, B.B. & Kaygusuz, A. & Karabiber, A., 2012. "A user-mode distributed energy management architecture for smart grid applications," Energy, Elsevier, vol. 44(1), pages 167-177.
    14. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2002. "Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks," Applied Energy, Elsevier, vol. 71(2), pages 87-110, February.
    Full references (including those not matched with items on IDEAS)

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