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We present a detailed review of various Home Energy Management Schemes (HEM,s). HEM,s will increase savings, reduce peak demand and Pto Average Ratio (PAR). Among various applications of smart grid technologies, home energy management is probably the most important one to be addressed. Various steps have been taken by utilities for efficient energy consumption.New pricing schemes like Time of Use (ToU), Real Time Pricing (RTP), Critical Peak Pricing (CPP), Inclining Block Rates (IBR) etc have been been devised for future smart grids.Home appliances and/or distributed energy resources coordination (Local Generation) along with different pricing schemes leads towards efficient energy consumption. This paper addresses various communication and optimization based residential energy management schemes and different communication and networking technologies involved in these schemes.
In this paper we present a systematic review of various home energy management (HEM) schemes. Employment of home energy management programs will make the electricity consumption smarter and more efficient. Advantages of HEM include, increased savings
A significant amount of research has been conducted in order to make home appliances more efficient in terms of energy usage. Various techniques have been designed and implemented in order to control the power demand and supply. This paper encompasse
Smart home devices are vulnerable to passive inference attacks based on network traffic, even in the presence of encryption. In this paper, we present PINGPONG, a tool that can automatically extract packet-level signatures for device events (e.g., li
Demand side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. To provide incentives for consumers to shift their consumption to off-peak times, the utility company charges consumers differential pricing f
In Federated Learning (FL), a global statistical model is developed by encouraging mobile users to perform the model training on their local data and aggregating the output local model parameters in an iterative manner. However, due to limited energy