Author(s)

AHMED AL-ADAILEH, SOUHEIL KHADDAJ

Abstract

The planet’s resources experience fundamental troubles and unjust utilization. A large portion of the destruction is set off by using the planet’s resources to produce energy of all kinds. To help with reverting the situation, there are mainly two approaches: firstly, to consider generating energy from clean and renewable resources, and secondly, to reduce the consumed energy by applying energy management systems. Due to the high energy consumption within the household sector, this paper aims to propose a dedicated household framework that tracks, predicts and manipulates the energy consumption of almost all appliances in the household; a sample household appliance is used to illustrate the main approach. The system is capable to track energy consumption and other related data directly from smart household appliances using their native connectivity and application programming interface capabilities, or from conventional appliances after equipping them with appropriate sensors and various Internet of Things (IoT) hardware. Once enough energy data is gathered, machine learning technologies will be applied to enhance the dataset and establish a solid background to predict energy consumption and apply the most suitable strategy from the available three strategies which most fits the appliance category. A case study implemented on a sample household appliance shows a possibility of reducing energy consumption by up to 22% by making a decision to replace the appliance with a more efficient one.

Keywords

household energy, household sector, IoT, predictive analysis, machine learning