تلخیص
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 predefined 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.
Ubaid ur Rehman . (2021) Artificial Neural Network based Demand Side Management for Smart Grids, Journal of Space Technology , Volume 11, Issue 1.
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