Smart home energy management using pervasive AI 

Supervisors 

A/Prof Muhamad Risqi Utama Saputra (Monash University) 

A/Prof. Markus Wagner (Monash University) 

The project 

As electricity demand continues to rise in Indonesia, inefficient energy use in residential and office settings significantly contributes to carbon emissions, resource depletion, and excessive strain on the electricity grid. If left unaddressed, this increasing pressure on the grid can lead to higher electricity costs, greater reliance on fossil fuels, and an increased risk of power outages. To mitigate these challenges, optimising home energy consumption is crucial for enhancing environmental sustainability and accelerating the transition to green energy. 

This research explores the development of Pervasive AI, a seamless integration of AI, intelligence sensors and Internet-of-Things (IoT) into everyday environments, to enhance energy efficiency in residential and office environments, including homes equipped with solar panels. By leveraging real-time data from smart home devices, weather forecasts, and user behaviour patterns, the AI system will generate dynamic recommendations to optimise energy consumption, ensuring maximum efficiency without compromising comfort. For homes with solar panels, the algorithm can be designed to prioritise solar energy utilisation, reducing dependence on the grid.

To achieve those objectives, this research will develop an intelligent system that will learn to understand user behaviour (including energy usage behaviour), predict energy demand, optimise appliance scheduling, and provide actionable insights to help home and building owners minimise waste. By integrating machine learning models with IoT-enabled sensors, this study will create a scalable framework for smart home energy optimisation, demonstrating the economic and environmental benefits of AI-powered energy management in modern offices and households.