Machine Learning for Intelligent Energy Consumption in Smart Homes

The growth of personal pleasure is a direct result of a person's ability to provide themselves with energy. Since people may construct and enhance their way of life more swiftly with current innovation, valuable energy has become a sought-after expansion for many years due to the utilization of smart houses and structures. The demand for energy is greater than the supply, resulting in a lack of energy. In order to keep up with the demand for energy, new strategies are being developed. Many areas' residential energy use is between 30 and 40 percent. There has been an increase in the need for intelligence in applications like as asset management, energy-efficient automating, safety, and healthcare monitoring as a result of smart homes coming into existence and expanding. Energy consumption optimization is being tackled with the use of an energy management approach in this study. There has been a recent surge in interest in data fusion in the context of building energy efficiency. Accuracy and miss rate of energy consumption predictions were calculated utilizing the data fusion technique presented by the proposed study. Simulated findings are being compared with those of previously reported methods. It also has a prediction accuracy of 92 percent, which is greater than that of any other technique that has been previously reported. It's becoming increasingly important for households to keep their power costs down as the amount of electricity they consume rises and dispersed new energy sources are introduced. The installation of a home energy management system is a practical solution to these issues.