Model predictive control technique for energy optimization in residential appliances

Several governments are phasing out coal fired generation power plants to reduce greenhouse gas emission. At the same time, nuclear generating facilities are reaching the end of their life and in the wake of the Fukushima disaster, developed countries have chosen to phase out nuclear energy early in the 2020s, removing 15% of the most stable and reliable portion of their energy mix. These two reasons create an urgent need to add new generating capacity or reduce consumption during peak periods, or both. The first option for power generation is the use of renewable energy resources, which can inject power to the grid without greenhouse gas emissions. But, the capacities of renewable energy resources are not enough to supply all the required power from the load side. All of these facts are leading to the proposal of novel approaches to reduce the utilization of energy in different sectors i.e. in residential, commercial, agricultural and/or industrial sectors to reduce the customer's total energy costs, energy demand, especially during on-peak, and greenhouse gas emissions, while taking into account the end-user preferences. The main objective of this paper is to demonstrate the impact of optimization technologies on energy savings of residential households. In this regard, a model-based predictive control approach is proposed for home cooling and heating systems. Its effectiveness is compared to thermostat conventional control by providing simulations upon 24 hours in a household.