Smart grid applications are becoming increasingly popular, as they aim to meet the energy requirements with innovative solutions by integrating the latest digital communications and advanced control technologies to the existing power grid. In smart metering management systems, several incentives, such as demand response programs, time-of-use and real-time pricing, are applied by utilities in order to encourage customers to reduce their load during peak load hours. However, it is usually a hassle for residential customers to manually respond to prices that vary over time. To overcome this limitation, this paper presents an Artificial Neural Network (ANN) as a support for a Home Energy Management (HEM) system based on Bluetooth Low Energy (BLE), called BluHEMS. The proposed mechanism is able to forecast the energy consumption conditions, i.e., to predict the home energy requirements at different times of the day or on different days of the week. The paper provides a detailed description of the ANN configuration, an analytical analysis on the embedding parameters for the derivation of best performance conditions values, and simulative assessments, obtained through Matlab and NS-2 simulations.

"An Innovative Approach for forecasting of Energy Requirements to improve a Smart Home Management System based on BLE" - IEEE TGCN was born from three issues of Series on Green Communications and Networking published on IEEE Journal on Selected Areas in Communications - ISSN 0733-8716.

COLLOTTA, MARIO
;
PAU, GIOVANNI
2017

Abstract

Smart grid applications are becoming increasingly popular, as they aim to meet the energy requirements with innovative solutions by integrating the latest digital communications and advanced control technologies to the existing power grid. In smart metering management systems, several incentives, such as demand response programs, time-of-use and real-time pricing, are applied by utilities in order to encourage customers to reduce their load during peak load hours. However, it is usually a hassle for residential customers to manually respond to prices that vary over time. To overcome this limitation, this paper presents an Artificial Neural Network (ANN) as a support for a Home Energy Management (HEM) system based on Bluetooth Low Energy (BLE), called BluHEMS. The proposed mechanism is able to forecast the energy consumption conditions, i.e., to predict the home energy requirements at different times of the day or on different days of the week. The paper provides a detailed description of the ANN configuration, an analytical analysis on the embedding parameters for the derivation of best performance conditions values, and simulative assessments, obtained through Matlab and NS-2 simulations.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11387/122462
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