Arquitectura Big Data para edificios inteligentes basada en herramientas Open Source enfocada en la gestión energética

Abstract

The development of people's lives around technology and the evolution of technology have led to the massive generation of information. It involves millions and millions of data coming from terminals, sensors, and social networks that have driven applications capable of leveraging this information to offer better services and improved living conditions. These improvements rely on the analysis of information. However, traditional techniques for managing and analyzing information have become insufficient and obsolete in the face of the immense amount of data. This is where Big Data emerges as a solution for managing large quantities of data.

In this case, the problem to be solved is establishing a Big Data architecture for the production, consumption, storage, presentation, and analysis of information generated by energy consumption in a smart building context, using open source techniques and tools.

To address this, a Big Data architecture is studied and developed using open source tools. For the solution, a measurement ingestion system is implemented to collect measurements of interest such as voltage, current, and power communicated through the Modbus RTU protocol to a microprocessor. The microprocessor is responsible for making the information available in real-time using the real-time streaming platform called Apache Kafka. Once the information is available, it can be accessed from a system based on the Linux operating system, where it will be stored in SQL and NoSQL databases, graphically represented, and statistically analyzed. The expected impact is to create a reference source regarding open source Big Data systems.

The system consists of a measurement system connected to a microprocessor that sends and makes the information available. The data will be accessed from a computer that stores the data and will be graphically represented and analyzed using open source tools focused on Big Data.