Energy data collection system combined with Energy Utilities

Submitted by Angelo Assis on Tue, 03/17/2020 - 17:32

Energy data collection involves a combination of consumption metering processes that establish limits and trends for the data. In this article we will discuss the relationship of the energy data collection system with energy and utilities management. Check it out!


Identifying the largest consumer within an industrial plant is a recurring concern of large users of energy. This is because the analysis of energy consumption is a complex task that involves advanced statistics-based techniques. Therefore monitoring the metering of energy consumption using a data collection system guarantees greater agility in process analysis and more robust data collection.

The energy data collection system is responsible for collecting the energy measurement data, which can be performed manually or automatically. In addition, using the system it is possible to establish limits and trends for the data collected. In the case of companies that are large consumers of energy and therefore have a significant volume of consumption data and information to be analyzed, the use of an energy data collection system is required, because using such a collection system it is possible to predict consumption, generate alerts, and save energy.

How does data collection work?

The data collection process can be a complex task due to the large number of sources. This is because it is very common to have data historians and PIMS (Plant Information Management Systems) from different suppliers, which makes it more difficult to analyze and understand the data collected. Therefore the process should be able to integrate a variety of data sources, identifying, for example, the intervals that have no data (gaps), extrapolate the limits, and find sudden variations. Therefore a collection system is able to optimize data processing and, in particular, cleanse the information.

The first part of the process is capturing the data directly from its source. As there are a variety of sources and different types of elements in each, it is necessary to cleanse and normalize the data so that it can then be stored in a Data Lake. With the data treated and structured it becomes possible to perform queries and analyses to evaluate consumption models and create customized alerts and events.


Figure 1 – Data collection system

The data collection and analysis system is something crucial for any process within a company. Therefore ensuring the quality of data – careful monitoring, evaluation of information consistency, and checking the sources of the facts – can significantly impact the results. Data Quality is responsible for data treatment – defining limits, goals, variations, and trends, allowing analyses to be performed with greater accuracy.

Why collect?

The functions of an energy data collection system go beyond gathering information of metering, generation, and consumption of energy. The system should be able to perform a complete analysis of contracts, production, and budgets, allowing integration of the entire metering process into a single system and enhancing the effectiveness of the data collection process.

The use of an energy data collection system is required to analyze and, above all, understand the collection process. From more advanced studies it is possible to obtain descriptive analyses (what is the problem and its cause), predictive analyses (why the problem happened and what will happen next), and prescriptive analyses (how will we be impacted and what should we do?).

The system also allows analyses to make more accurate and coherent predictions.

Also read: The power of data analysis in energy management

  • The main benefits of an energy data collection system
  • Integration of the entire measurement process into a single system
  • Increased information about the collection process
  • Reduction of time for resolving incidents in collection processing
  • Reduction of operational effort

Because it is a complex and extensive analysis, the collection process involves the management of large databases with an ever-increasing flow of information and should therefore pressure the suppliers of this type of solution to increase capacity and performance. Starting from this point, Big Data has increasingly helped companies with database management, and it is precisely in this context that the concept of Data Lakes enters the picture.

Data Lakes are data processing platforms designed to store and analyze large quantities of information originating in different data sources. Using this technology, different types of data are stored in their original form, and from there are extracted, combined, correlated, and utilized in different ways according to the needs of each business.

Also read: How data lakes may help energy & utilities management

Collection of energy data and energy and utilities management

Concepts like Data Lakes and Big Data fit very well in the context of energy and utilities management due to the wide range of data types within a single industrial plant. The collection and recording of metering data are used to create temporal series of different frequencies starting from different meters. This analysis allows the handling of isolated problems such as management of energy and utilities supply contracts, costs, budgeting, and consumption. In addition, the architecture based on Data Lakes and the advance of incorporated new technologies permit the integration of systems and the production of new information and more precise analyses.

The information obtained from the energy data collection system can be used to detect problems and inefficiencies as soon as they occur. With real-time monitoring it is possible to fully visualize the behavior of the data, which can directly influence the energy supply strategy.

The opportunities for innovation are thus limitless, as are the potential improvements in energy management and efficiency.

Read more: Using contextual information to increase the power of analyses of energy performance

Gerente de desenvolvimento, Viridis

Software Development Manager at Viridis, holding a Master's degree in Computer Science from Universidade Federal de Minas Gerais. Angelo has been working for more than eight years with software and systems engineering, and is also a lecturer at the Institute of Management and Information Technology at Belo Horizonte, teaching MBA classes.

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