The energy industry faces a variety of challenges as a result of the growing demand for electricity. The emphasis is shifting to optimizing energy use in residential settings so as to achieve sustainable alternatives. The escalating demand for sustainable energy practices in residential environments gives rise to innovative approaches to home energy management. In order to significantly reduce home energy use and contribute to a more sustainable future, this paper proposes an optimization model for home energy management that combines Model Predictive Control (MPC) with Demand Response (DR) strategy to reduce energy consumption. The study used several types of data, such as the hourly load demand of a house and solar irradiance data. Load demand profile, derived from historical electricity usage records, provided hourly energy consumption over a 24-hour period, serving as essential input for predicting future energy needs using the MPC algorithm. Solar irradiance data and PV system specifications were utilized to model the power generated by PV panels, while information about the battery energy storage system, including its capacity, efficiency, and state of charge (SOC) limits, was essential for modelling the behavior of the battery in storing and discharging energy. The model encompasses mathematical models and optimization tools for the efficient usage of photovoltaic (PV) panels, battery energy storage systems (BESS), and grid power. With the aid of MATLAB/Simulink simulations, the study demonstrated that MPC effectively predicts energy demand and allocates power sources effectively, achieving a 41% reduction in energy costs compared to grid-only scenarios. Considering the results obtained, this paper suggests areas of further research work, such as integrating dynamic pricing models in countries like Nigeria and exploring hybrid renewable energy systems. This will build on the findings obtained in this work and further improve household energy efficiency and sustainability.
Omeje et al. (Thu,) studied this question.