Abstract:
This research study describes an integrated Home Energy Management System
(HEMS) that uses thermal models and Model Predictive Control (MPC) to optimise
energy use for user comfort, power availability, cost reduction, and equipment
longevity. The system also schedules flexible, deferrable, and thermal appliances
based on load and utilisation characteristics, considering the energy demand for
the whole household. The HEMS accurately predicts and controls indoor temperatures
using a thermal model, while the MPC framework optimises energy
consumption using real-time weather forecasts. Load scheduling algorithms prioritise
and manage devices based on their load characteristics and importance. In
a laboratory, hardware implementation of the thermal model was monitored and
controlled, including interior temperature and power availability, to assess the suggested
method. Control activities affected user convenience, energy efficiency, and
device operation. Results show that MPC and thermal models improve HEMS energy
management. The technology efficiently controls indoor temperatures while
maximising energy efficiency. Load scheduling optimises device operation, reducing
energy waste and improving system performance. This work integrates thermal
models, MPC, and load scheduling to improve household energy management.
The findings enable energy-efficient residential building design, user comfort, and
device optimisation.
