Intelligent Energy Management in Smart Buildings Using AIoT

Abstract

The escalating national demand for energy and the need for the proper implementation of sustainable consumption practices have prompted the investigation and development of an intelligent energy management system using AIoT. Rapid advancements have provided a wide variety of technological solutions to prominent issues facing society. However, sustainable energy distribution remains a significant concern; one made more pressing by the climate change crisis. This paper explores the implementation of a Soft Actor Critic model for energy cost reduction while maintaining occupant comfort to optimize energy consumption in a smart building. This will form part of a larger AIoT system integrating renewable energy and data collection via MQTT. The system seeks to evaluate the effectiveness of intelligent energy management in providing sustainable financial benefits to the individual customer and a sustainable power economy to the Ghanaian market at large. Additionally, the system integrates a Time of Use pricing model to suggest prime energy usage periods to the user for the encouragement of off-peak hour consumption, suggesting TOU implementation in Ghana. Experimentation results suggest a SAC agent-controlled single family house would benefit from cost savings while reducing the emissions caused as a result of the pattern of electricity demand from the house. This integration not only improves convenience and energy optimization but also provides a financial incentive. Incorporating AIoT with reinforcement learning improves system adaptability and offers a scalable solution for applications in future smart grids.