Abstract
Smart Grid (SG) deployment is a globally emerging trend it has been proved that the technology has massive potential to properly manage and communicate the load profiles data generated within the decentralized power networks. The appropriate use and maneuvering of this vigorous data are the main obstacles involved between the large-scale implementation of SGs. Therefore, the Demand side management (DSM) techniques are usually employed to optimize SG in real time. In this paper we have proposed a novel technique to the appropriate DSM scheme for SG management and present the simulation results which has been carried out by using the power consumption data collected through advance metering infrastructure (AMI). Our proposed method forecasts the consumers load curve patterns and uses these pre-forecasted power consumption patterns data to train and substantiate an Artificial Neural Network (ANN) which then governs the SG process, after which the method continuously repeats this process and uses the pre_x0002_defined load computation patterns to categorize newly broadcasted power procurement data. The obtained result from this research proves that, our proposed optimization method intelligently assists the ANN based DSM network, and the extensive performance evolution by simulations shows satisfactory results while classifying the load curves.
Keyword(s)
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