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An Innovative Approach for Forecasting of Energy Requirements to Improve a Smart Home Management System Based on BLE

机译:一种创新的能源需求预测方法,以改进基于BLE的智能家居管理系统

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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, 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. This 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.
机译:智能电网应用正变得越来越流行,因为它们旨在通过将最新的数字通信和先进的控制技术集成到现有电网中来通过创新的解决方案来满足能源需求。在智能电表管理系统中,公用事业公司采取了多种激励措施,例如需求响应程序,使用时间和实时定价,以鼓励客户在高峰负荷时段减少负荷。但是,对于住宅用户来说,手动响应随时间变化的价格通常很麻烦。为了克服这一限制,本文提出了一种人工神经网络(ANN),以支持基于低能耗蓝牙技术的家庭能源管理(HEM)系统,称为BluHEMS。所提出的机制能够预测能量消耗状况,即,预测一天中的不同时间或一周中的不同天的家庭能量需求。本文提供了ANN配置的详细说明,对嵌入参数的分析分析以得出最佳性能条件值,以及通过MATLAB和NS-2仿真获得的仿真评估。

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