Far beyond buzzwords, understand how concepts of Industry 4.0 like Internet of Things, Big Data and Machine Learning contribute to energy and utilities management.
The term Industry 4.0 has continued to gain strength. What many people don't realize is that the term was coined in a strategic initiative of the German government, called Industrie 4.0, whose principal objective was to “drive digital manufacturing, promoting interconnection between products, value chains and business models.”
Indeed, we have come to recognize Industry 4.0 as the "digital transformation of industrial markets, with intelligent manufacturing in the front line. Industry 4.0 also represents the so-called Fourth Industrial Revolution in discrete manufacturing and of continuous processes, in logistics and in supply chains (Logistics 4.0), in the chemical industry, energy, transportation, sectors like oil and gas, mining and metallurgy, in addition to other industries such as natural resources, health, pharmaceuticals and even intelligent cities."
But to go beyond the jargon, we will explore the main concepts and technologies related to Industry 4.0 in the context of energy and utilities management.
1. Extensive monitoring
The development of technologies for instrumentation and monitoring of industrial processes enables data capture in ever-increasing resolutions, allowing increasingly powerful analyses. In energy and utilities management, sophisticated physical meters (instruments) are capable of interpreting physical quantities that allow the understanding of processes of interest, monitoring variables that range from applied power, for example, to harmonics that describe the quality of the electricity consumed.
In addition to technological advances, the costs of acquisition and installation of modern sensors and instruments have become increasingly accessible, allowing broad and deep understanding of the characteristics of industrial processes of interest, allowing redundancy of measurements and the obtaining of high-quality data – essential for planning, control, and improvement of energy efficiency and operational efficiency.
2. Industrial Internet of Things (IIoT)
The Internet of Things is another widely-discussed concept, and refers to an entire “network of physical devices that include sensors, actuators, electronics, and connectivity, allowing the integration of the physical world with computer systems.” In our context, the Industrial Internet of Things, a term often used as a synonym for Industry 4.0, refers to the application of technologies such as Machine Learning and Big Data to exploit sensor data, communication between machines (M2M) and automation systems to improve industrial and manufacturing processes.
In energy and utilities management, Industry 4.0 is realized in the connectivity between measuring instruments and the entire information and automation architecture of industrial organizations, extending the capacities for collection, communication, and storage of large volumes of data related to the consumption, generation, and transformation of energy inputs.
3. Analysis of large volumes of data
Typical industrial applications can involve thousands of meters collecting data at high frequencies, generating gigabytes of data each day – in energy quality applications, for example, specialized meters today visualize the network each millisecond.
This abundance of data and the increasing availability of computational resources allows the application of specific techniques of artificial intelligence with the aim of facilitating the prediction of variables and the identification of patterns of interest in a range of industrial processes.
Due to the very nature of the phenomena that produce data collected from industrial operations and the limitations of the instruments that are used to capture them, the development of prediction models based on data collected from industrial operations involves considerable levels of noise and imposes additional pressures on the volume, variety, speed, and veracity requirements of the data, something common to Big Data applications. Efficient algorithms for processing data quality are thus becoming as essential as algorithms for the construction of prediction models.
In energy and utilities management, the data available can give rise to, for example:
- prediction models for energy consumption (or energy generation) of operations, starting from planned production levels or other contextual variables;
- models for learning and establishing the ideal modes of operation, which permit effective levels of energy consumption;
- models for analyzing the energy efficiency of processes, starting from the capture of entry and exit variables and knowledge of the transformation phenomena involved.
4. Efficiency and sustainability
Behind the entire investment in Industry 4.0 lies a common objective: increasing the efficiency and competitiveness of an operation. The benefits are direct and carry the potential to establish a virtuous cycle of investment, result and reinvestment: more competitiveness results in better financial results; with more cash in hand, more investments can be directed to capacity expansion, productivity technologies, operational efficiency and energy efficiency; greater efficiency ensures lower levels of greenhouse gas emissions, reducing environmental impact in addition to improving the quality of work, both of which positively impact the community.
Industry 4.0 and energy and utilities management
Energy management is one of the main pillars of Industry 4.0. The motivation comes from a combination of environmental aspects, cost pressure, and regulation as well as the proactiveness of organizations when it comes to efficient consumption of energy and utilities.
In addition, the integration of different sources of energy generation in an increasingly demanding and distributed market will require management technologies capable of recognizing, predicting and acting in a way to guarantee quality, sustainability, and efficiency, including costs, in energy consumption.
Modern energy and utilities management systems should be able to exploit a large volume of data collected by various types of meters on a number of variables of interest for a certain industrial operation, assembling the above concepts – extensive monitoring, the Industrial Internet of Things, analyses of large volumes of data, and efficiency and sustainability – around a common, integrated, and robust objective.