Artificial Neural Network-Based Smart Energy Meter Monitoring and Control Using Global System for Mobile Communication Module

This paper presents smart and optimal way of allocating power to the utility using global system for mobile communication module-based remote automatic energy meter reading system. The designed device is installed with the energy meter at consumer premises. A smart communication is established between service provider and consumer using GSM module which is capable of calculating the energy consumed at different tariff and time. An artificial neural network using back-propagation approach is employed to obtain optimal allocation of service provider to meet the objective function. The novel idea of smart energy metering not only reduces the cost of energy consumption but also helps in proper repayments, optimal usage of power based on time of day tariff, and theft control with higher reliability and greater flexibility. A smart real-time prototype of the automatic energy reading system was built to demonstrate the effectiveness and efficiency of automatic meter reading, billing, and notification through the use of global system for mobile communication network.

[1]  Rosario Morello,et al.  A Smart Power Meter to Monitor Energy Flow in Smart Grids: The Role of Advanced Sensing and IoT in the Electric Grid of the Future , 2017, IEEE Sensors Journal.

[2]  S. N. George,et al.  GSM based automatic energy meter reading system with instant billing , 2013, 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s).

[3]  Ling Zou,et al.  The design of prepayment polyphase smart electricity meter system , 2010, 2010 International Conference on Intelligent Computing and Integrated Systems.

[4]  N. Malik,et al.  Experimental study and design of smart energy meter for the smart grid , 2013, 2013 International Renewable and Sustainable Energy Conference (IRSEC).

[5]  P. Rakesh Malhotra,et al.  Automatic Meter Reading and Theft Control System by Using GSM , 2013 .

[6]  Zachary Chase Lipton A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[7]  Yoshua Bengio,et al.  Gated Feedback Recurrent Neural Networks , 2015, ICML.

[8]  Amy Loutfi,et al.  A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..

[9]  Ue-Pyng Wen,et al.  A review of Hopfield neural networks for solving mathematical programming problems , 2009, Eur. J. Oper. Res..

[10]  Vinu V. Das,et al.  Wireless Communication System for Energy Meter Reading , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.