Residential Energy Optimization Algorithms

How much energy can be saved by opening a window instead of running an air conditioner? How does this affect the air quality of a home? With rising energy costs and as concern for the environment grows more widespread, the need to conserve energy resources is becoming more apparent. Although many alternative energy technologies have been introduced, these can prove to be nearly useless without the proper utilization. Some systems exist that give users an estimate of the total energy consumed, but this is not conveyed in a method useful to a homeowner. Complex systems able to solve both problems exist, yet the level of difficulty of installation and operation do not allow for every homeowner to use them. The proposed innovation is the development of an adaptive energy management system that will use a series of calculated optimization algorithms to suggest or execute energy saving actions. A computer will automatically collect and analyze data to determine and locate energy losses and wasted energy in the building. These optimization algorithms will learn users’ habits and use historical data to instantly analyze building energy needs to reduce energy consumption. By using this information, a home can be managed and controlled more efficiently by manual (suggesting) and automatic (executing) management. This innovation will provide the basis for homeowners to easily save energy based on their usage requirements. Brought to the full extent, if the majority of homes in America were equipped with this system, it could reduce total energy consumption by a very significant amount. The objective for this research is to develop a series of automatic algorithms for the following: 10 system controls that have a high potential for improvement in residential energy conservation; five system controls that have a high potential for improvement in indoor air quality; heating and air conditioning performance metric determination; maximum HVAC efficiency based on power consumption and performance; energy production predictions based on a photovoltaic system and weather forecasts; the determination of optimum times to run a dishwasher, clothes washer, and clothes dryer; and determining conditions in which it is more efficient of utilize passive cooling (versus active cooling). By setting sensors in sample homes and collecting data correlating to energy consumption for a period of time, an algorithm will be programmed to optimize energy consumption in a building. These algorithms will be tested to ensure greatest optimization.