Automation of Thermal Energy Storage in Homes Using Artificial Neural Networks

About 60% of the energy consumed by homes in North America is for air conditioning. With about 78% of electric energy is generated by from fossil fuels in the US, this energy use contributes to greenhouse gas emissions and global warming. Residential solar energy is now becoming cost effective and is as cost effective electric energy from the electric grid. However, solar energy availability and energy required for air conditioning are mismatched with respect to time. This mismatch in availability and need necessitates the use of energy storage. In previous works, storage of energy in thermal air mass of homes has been proposed. However, the thermostat required for such application is very complex. In this work, an artificial-neural-network-based thermostat is proposed. A method to train the model for an average home is demonstrated with an example, and the method is shown to be effective.