2020
DOI: 10.1088/1757-899x/993/1/012091
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IoT based energy efficient architecture for integrated Smart Grid

Abstract: The emergence of Smart Grid (SG) plays a vital role in energy generation and distribution system. As SG is the association of numerous applications, this can be properly utilized to reduce the energy consumption. Hence, in this work, a new architecture is proposed to optimize the usage of RES effectively. This proposed architecture utilizes IoT for gathering the power consumption profile of the devices. Based on this profile, a schedule for a device is generated by the Micro Grid. The analysis shows the effici… Show more

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Cited by 5 publications
(1 citation statement)
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“…e function of the input layer is to receive the input model data and transmit it to the hidden layer. According to the residential energy-saving lighting requirements, it can be seen that the efficiency of lamps, luminous flux of light source, average reflectance ratio of wall surfaces, the installation height of lamps, working area, maintenance coefficient of lamps, effective floor reflectance, and effective ceiling reflectance have a significant impact on residential energy-saving lighting [17][18][19][20][21]. erefore, the above 8 types of data are selected as input data, and the number of neurons in the input layer is 8.…”
Section: Neurons Of Input Layermentioning
confidence: 99%
“…e function of the input layer is to receive the input model data and transmit it to the hidden layer. According to the residential energy-saving lighting requirements, it can be seen that the efficiency of lamps, luminous flux of light source, average reflectance ratio of wall surfaces, the installation height of lamps, working area, maintenance coefficient of lamps, effective floor reflectance, and effective ceiling reflectance have a significant impact on residential energy-saving lighting [17][18][19][20][21]. erefore, the above 8 types of data are selected as input data, and the number of neurons in the input layer is 8.…”
Section: Neurons Of Input Layermentioning
confidence: 99%