Personalized Energy Management Systems for Home Appliances Based on Bayesian Networks

Abstract

In Japan, the electricity consumption of household domestic appliances is increasing. The introduction of the demand response (DR) framework will promote electricity consumption reductions in the household sector by limiting electricity usage and by regulating the price of electricity. Because the appropriate operation patterns differ for each user under the DR programme, a home energy management system (HEMS) will play an important role by considering the priority of various home appliances and by appropriately modifying appliance operations to minimize the impact on a user's lifestyle. In this study, HEMS based on the Bayesian network is proposed; the system will learn the behaviour of a user and prioritize the operation of home appliances under restrictions on electricity consumption.

Multi-criterion Analysis of Selected Power Management Strategies in Smart Home Systems

“…With the development of technology, especially the Internet of Things (IoT), almost every electronic element in a house can now be integrated with a smart home system via wireless communication, i.e. by network, machine-machine communication or cloud processing [Byun, Hong and Park 2012;Shoji et al 2015;Arvind, Raj and Krishna Prakash 2016;Krishna Prakash and Surjith 2017;Yogavani and Krishna Prakash 2017]. Energy efficiency is the highest priority in designing smart home systems.…”

Intelligent approach for residential load scheduling

“…The authors in [17] developed mathematical models of storage devices and thermal loads such as batteries, water heater, and air conditioner for dynamic optimisation of their operation considering the users’ comfort constraints. A Bayesian network‐based home energy management framework under the demand response programme was presented in [18] to determine the optimal setpoints of thermostatic loads and the operation of the battery storage system. Thermostatic loads such as water heaters, air conditioners as well as battery energy storage systems were considered in this study where the proposed model considers the users’ lifestyle besides the environmental factors to manage energy consumption and satisfy user's comfort.…”