Energy Optimization Algorithm for Reducing Energy Consumption in a Smart Home

The importance of electricity to humanity cannot be overstated. The dependence of human beings on this very precious resource has caused a strain on its production as the growing demand is far outweighing the speed at which the supply can be ramped up. Furthermore, this explosion of demand has caused an even more significant problem of price gouging leading to costly utility bills and energy insecurity for the residential consumers. However, in recent years, the enormous leaps made in smart grid technology and internet of things (IoT) have brought a renewed enthusiasm in the research space as these two technologies when combined, have brought about breakthroughs in smart home automation. The primary focus of this study is to develop a smart home automation algorithm that can be used to reduce electricity consumption at home. The algorithm combines two essential electricity optimization methods which are appliance scheduling and environmental condition to provide an optimal energy consumption pattern. Sensor data from PIR, ambient light and temperature sensors are fed real-time into the algorithm to make appropriate decisions regarding appliance status. When this algorithm was simulated on a modeled home using a virtual human behaviour table, 68% energy saving of the maximum energy consumption of the house was achieved. This shows that the algorithm can provide a substantial energy savings which vary from consumer to consumer depending on their movement while in the house, specified environmental conditions and preset appliance schedule.