Energy Consumption Prediction of Smart Buildings by Using Machine Learning Techniques - Haboubi, Sofiene; Ben Salem, Oussama (Iraqi Journal of Science, 2023)
This paper presents an IoT smart building platform with fog and cloud computing capable of performing near real-time predictive analytics in fog nodes. The researchers explained thoroughly the internet of things in smart buildings, the big data analytics, and the fog and cloud computing technologies. They then presented the smart platform, its requirements, and its components. The datasets on which the analytics will be run will be displayed. The linear regression and the support vector regression data mining techniques are presented. Those two machine learning models are implemented with the appropriate techniques, starting by cleaning and preparing the data visualization and uncovering hidden information about the behavior of the smart building appliances using the energy consumption feature. Afterwards, the implementation of two regression models to predict total energy consumption began. On a hospital database, the two techniques' performances are compared and validated. The results achieved are promising and prove the reliability of the IoT smart building platform.