Energy Prediction in Edge Environment for Smart Cities

People around the world are trending to the Internet of Things (IoT) technologies. A large number of IoT devices are installed every day to enhance the sophistication and sustainability of smart cities. Besides, a smart city needs a smart energy management system including a smart grid, smart building. Also, a smart energy distribution system is important to reduce energy and manage it efficiently. The IoT devices are installed in various buildings in the city, they use a lot of energy, and produce energy usage information. In the existing cloud system, it is difficult to analyze and transfer the data quickly, similarly impossible to receive the analysis result immediately. However, edge computing has the advantage of fast data analysis and supply analyzed results to the field. In this process, data is processed in the edge environment, where data has been collected, analyzed, and processed in the edge nodes. In this study, we presented an energy prediction model based on the edge computing technique. We used a dataset where various environmental and energy use information has been considered. Also, we have used five different Machine Learning (ML) classifiers to classify the prediction model and assess the prediction performance. This study presents an energy prediction model using various ML classifiers in an edge computing environment.