Intelligent Power Management System Using Hybrid Renewable Energy Resources and Decision Tree Approach

Abstract

Optimal power usage and consumption require continuous monitoring, forecasting electric energy consumption and renewable generation. To facilitate integration of renewable energies and optimize their resources, new communication and data processing technologies are used in new projects. This article shows the works and results obtained in the eoTICC project. The objective is to design and develop an intelligent energy manager using the Archimedes wind turbine and a solar generation system, both integrated in industrial and residential power facilities. Solutions based on Artificial Intelligence paradigms and Internet of Things protocols allow automatic decision making to optimize energy management. In a facility, the energy demand and weather forecasts can be known by an intelligent energy manager. With these conditions, the energy manager can develop rules based on decision trees to automate control actions aimed at optimizing the use of energy. This article shows the architecture of IoT infrastructure and the first rules designed in the project. The result obtained provides improvements in the use of renewable energy in current facilities that do not use this type of intelligent management. The improvements allow to use the energy at the time of generation, avoiding unnecessary storage.

Keywords

• smart energy management
• decision tree
• internet of things