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A platform for research: civil engineering, architecture and urbanism
Maximum likelihood based energy consumption estimation framework for smart grid
Smart implementation of the smart grid is one of the trending topics in the media for the emerging economy, which has been promoted by state and federal governments to prevent energy waste and theft. One of the key factors in improving smart grid performance is the design of ‘a smart grid node’ that includes combined use of various parts of that system including the cost of energy and energy consumption in an individual household power system. True estimate of individual energy consumption can be the key factor in improving smart grid performance. Hence, the goal of this research is to design an analytic model to predict a sustainability performance especially power consumption, using a probabilistic framework. The increase in the cost of energy is a significant factor in an individual household power systems. The proposed maximum likelihood estimation (MLE) algorithm is applied to estimate the probable energy uses that can control the transmission of the routing request for the least usage area in order to increase power supply and helps in reducing power supply that has the least usage area. In this proposed network, the (MLE) algorithm adopts the energy-efficient probabilistic control by simultaneously using the residual energy of each node in the context of the typical network. From the simulations results, the study concludes that implementing the cluster-based MLE algorithm in smart grid can help in maximizing the usage area for the consumed power supply. Keywords: Maximum Likelihood Estimator (MLE); Energy Consumption Framework; Cluster based MLE; Smart Grid.
Maximum likelihood based energy consumption estimation framework for smart grid
Smart implementation of the smart grid is one of the trending topics in the media for the emerging economy, which has been promoted by state and federal governments to prevent energy waste and theft. One of the key factors in improving smart grid performance is the design of ‘a smart grid node’ that includes combined use of various parts of that system including the cost of energy and energy consumption in an individual household power system. True estimate of individual energy consumption can be the key factor in improving smart grid performance. Hence, the goal of this research is to design an analytic model to predict a sustainability performance especially power consumption, using a probabilistic framework. The increase in the cost of energy is a significant factor in an individual household power systems. The proposed maximum likelihood estimation (MLE) algorithm is applied to estimate the probable energy uses that can control the transmission of the routing request for the least usage area in order to increase power supply and helps in reducing power supply that has the least usage area. In this proposed network, the (MLE) algorithm adopts the energy-efficient probabilistic control by simultaneously using the residual energy of each node in the context of the typical network. From the simulations results, the study concludes that implementing the cluster-based MLE algorithm in smart grid can help in maximizing the usage area for the consumed power supply. Keywords: Maximum Likelihood Estimator (MLE); Energy Consumption Framework; Cluster based MLE; Smart Grid.
Maximum likelihood based energy consumption estimation framework for smart grid
Mohd, Ajaz (author)
Miscellaneous
Electronic Resource
English
DDC:
690
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