An Innovative Approach for forecasting of Energy Requirements to improve a Smart Home Management System based on BLE
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.
A number of wired as well as wireless communications technologies for smart grid applications have been identified in recent years. However, in many of them the sheer number of communications links makes the use of wired solutions economically and/or physically prohibitive. On the other hand, wireless technologies offer benefits such as lower cost of equipment and installation, quick deployment, widespread access and greater flexibility , . To this end, several efforts have been made in order to explore suitability of specific wireless communications technologies, such as IEEE 802.15.4/ZigBee , IEEE 802.11/Wi-Fi , Bluetooth Low Energy (BLE) , for smart grid applications. Several approaches, based on wireless networks capabilities, have been introduced in the literature, with the aim to make intelligent decisions in residential buildings, in order to link demand response, TOU, and/or real-time pricing incentives with energy efficiency aspects , , . However, although the wireless network proposed in these works obtain good performance, the authors do not bother to choose the most appropriate wireless protocol. In fact, the choice of a wireless protocol with respect to another one may involve significant benefits and quite different results. For this reason, in other literature works, focused both on smart grids and on smart homes research fields, also a comparison among wireless protocols has been carried out. For instance, in  IEEE 802.15.4/ZigBee is compared with Wi-Fi and Bluetooth Classic, showing its performance. In  Wi-Fi technology for smart grid sensor networks is explored and results highlight the advantages of Wi-Fi over other wireless protocols when large data rate is required. In  a thorough analysis of wireless protocols for HANs is presented, comparing and discussing their advantages and downsides
Among wireless protocols, in these last years a novel research activity has focused on BLE because it has a high potential in becoming a key technology for smart homes in low power, low cost and small devices. In spite of its installed base in smart energy, home and building automation applications, IEEE 802.15.4 faces competition in BLE in these applications, as well. However, BLE has a lower energy consumption than IEEE 802.15.4 and, for this reason, may be a fine choice for home automation applications. On the contrary, IEEE 802.11 is used in devices where cost and low power are less important , when large data rate is required and as a wireless backbone combined with the other wireless technologies. This paper shows an improvement to BluHEMS (Bluetooth Low Energy Home Energy Management System)  and to its later version . In detail, BluHEMS is a novel energy management approach based on the communication among home appliances, a central energy management unit, a smart meter and an energy storage unit inside a smart home, through a wireless network based on BLE. The main limit of BluHEMS consists that it lacks in a system able to manage the scheduling of home appliances in a smart way, that is related to crisp variables in which the switching on/off choice may be taken based on the degree of membership of these variables in certain intervals (membership functions). This, along with the ability to use a mechanism able to manage the feedback of consumers, since they are Involved in the choice of switching on/off of home appliances, has led the authors to propose a new version of their approach  introducing a Fuzzy Logic Controller (FLC). However, the approach proposed in  has the limitation that it is not able to predict the home energy consumption and then bases its choices only taking into account the current value (recorded at run-time) of home loads (energy requirements). This, for instance, does not allow the consumers to use more energy at this time and then save it later.
In this work an Artificial Neural Network (ANN) for BluHEMS to deal with the problem of peak load management using the available data obtained by the Home Energy Management (HEM) system has been presented. The proposed mechanism provides the possibility 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 proposed approach is suitable for smart grid applications for effective demand side management, as it is able to rely on matching present generation values with demand by controlling the energy consumption of appliances and optimizing their operation at the user side. The paper proposed a deep analysis for the configuration of the ANN in order to obtain the one that achieves the best performance. As far as the implementation complexity, the embedding parameters have been analytically assessed and their values for best performance conditions have been derived. The paper also has provided extensive simulative assessments, performed through the Network Simulator Version-2 (NS-2), in terms of electricity consumption, load percentage and delay experienced by consumers for the proposed HEM solution, and in terms of packet delivery ratio, delay, and jitter for the wireless networks.
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