 Demand side management using artificial neural networks in a smart grid environmentM.N.Q. Macedo, J.J.M. Galo, L.A.L. de Almeida and A.C. de C. Lima Renewable and Sustainable Energy Reviews, 2015, vol. 41, issue C, 128-133 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. Keywords: Smart grid; Demand side management; Artificial neural network; Load profile classification (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:41:y:2015:i:c:p:128-133 Ordering information: This journal article can be ordered from DOI: 10.1016/j.rser.2014.08.035 Access Statistics for this article Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski More articles in Renewable and Sustainable Energy Reviews from Elsevier |
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