Predictive Analytics for Energy Consumption in Smart Homes with Fog and Cloud Computing Using Support Vector Regression
2022, vol.14 , no.1, pp. 49-60
Predict energy consumption in smart homes is our objective in this work. We defined the two predictive analytics techniques, and we will be tested in this study. We presented the Linear Regression and the Support Vector Regression data mining techniques. Implemented those two machine learning models with the appropriate techniques, starting by cleaning and preparing the data then we visualize it so that we can uncover hidden information about the behavior of the smart home appliances using the energy consumption feature. Afterwards, implementing the two regression models to predict the whole house energy consumption. Compared the performances of the two techniques. The results achieved are promising and proves the reliability of the IoT smart home platform.
Smart Home, Energy consumption, Cloud Computing, Fog Computing, Support Vector Regression, Support Vector Machine
Sofiene Haboubi, Oussama Bben Salem. Predictive Analytics for Energy Consumption in Smart Homes with Fog and Cloud Computing Using Support Vector Regression. International Journal on Information Technologies and Security, vol.14 , no.1, 2022, pp. 49-60.