Internet of Energy Data Analysis Using Machine Learning Techniques

Abstract

The energy sector encompasses essential processes such as the production, distribution, and consumption of energy. Traditionally, these processes have been managed through conventional networks, which often lead to issues such as process fluctuations, increased costs, and inefficiencies. However, the advent of Internet of Energy technology facilitates a transition from traditional to smart networks. In the Internet of Energy, the use of sensors results in the generation of large volumes of data. By employing machine learning to analyze this data, it becomes possible to make accurate predictions in the energy sector, which in turn supports effective decision-making for energy production and distribution. The objective of this study is to analyze data within the Internet of Energy using machine learning techniques, ultimately leading to the development of an artificial intelligence model capable of predicting energy consumption. Initially, previous models will be reviewed, and their outcomes will be compared and analyzed based on scores and evaluation metrics. Finally, a deep neural network model will be introduced, demonstrating an error rate of 0.3. The mean absolute error is reported as 0.4, and the mean square error is 0.3. Despite these advantages, there are also limitations to consider. The data involved in the analysis and prediction process must meet appropriate standards. The significant variability present in industrial processes adds complexity to the environment.