Design of embedded zigbee machine to machine smart street lighting system

Widodo, Djoko Adi; Iksan, Nur; Suni, Alfa Faridh

doi:10.1109/icitisee.2017.8285536

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…Researchers[6] developed M2M communication in the process of controlling and transferring measured energy data. Researchers[7] developed M2M communication networks on smart home and researchers[8] developed M2M communication networks on smart street lighting.…”

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confidence: 99%

Zigbee Communication on Machine to Machine Smart Home Micro Grid for Efficient Controlling System

Purwanto¹,

Hermawan²,

Suherman³

2020

Proceedings of the Proceedings of the 13th International Interdisciplinary Studies Seminar, IISS 2019, 30-31 October 2019, Mala

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Data communication between sensors and actuator devices with users in the energy monitoring and control system on the smart grid is very dependent on the existence of a gateway device. This causes the control process to be ineffective because it is very dependent on the Gateway as the communication center. For that we need an effective approach so that the communication process can take place only on the relevant device, i.e. using M2M technology. In its development, the use of M2M technology has challenges related to congestion, error communication and delay so that further research is needed. In this research the development of a smart home micro grid will use M2M technology which enables communication between devices in the smart grid area. One component needed in M2M communication is Zigbee which is a medium for data communication without wires. Zigbee performance is measured using RSSI and Throughput with scenarios without or with obstructions as well as scenarios through the selection of router configurations. Experimental results show optimal performance of zigbee with scenarios in obstacle areas at a distance of 35 meters and in areas without barriers at a distance of 60 meters. In the router configuration selection scenario, it can be concluded that direct configuration is the optimal choice rather than using a router. For this reason, when using a configuration through a router, the number of sending nodes must be minimized.

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mentioning

confidence: 99%

Zigbee Communication on Machine to Machine Smart Home Micro Grid for Efficient Controlling System

Purwanto¹,

Hermawan²,

Suherman³

2020

Proceedings of the Proceedings of the 13th International Interdisciplinary Studies Seminar, IISS 2019, 30-31 October 2019, Mala

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Renewable energy power generation forecasting using deep learning method

Widodo

Iksan

Udayanti

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

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Smart Micro Grid in household areas aims to meet electricity needs through the integration between state power plant with renewable energy sources so that the electricity used does not depend entirely on state utility. Smart Micro Grid also enables the availability of energy management services supported by Machine Learning (ML) technology, Big Data, Artificial Intelligence (AI), Internet of Things (IoT) and smart sensors so that consumer use of electricity is more efficient. To improve energy management services and distribution of renewable energy sources, new innovations in ML technology are needed to produce accurate learning models that can be used in the energy analysis process, such as monitoring, prediction, forecasting, scheduling and decision-making. However, the complexity of the problems in the smart grid system, which includes uncertainty and non-linearity, affects the more complex the energy data structure generated. Therefore, the simple ML method will not be able to perform the Learning process because it is limited to simple raw data processing. Therefore, the Deep Learning (DL) method can be used as a Learning method on data that has a complex and large structure. In this paper, Deep Neural Network (DNN) method will be developed using Long Short-Term Memory (LSTM) as a Learning model to provide Future Accurate Prediction (FAP) on electricity use and on renewable energy plants. Prediction test using Confusion Matrix accuracy value and RMSE error value

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