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Energy Optimization in Industry 4.0, often referred to as the fourth industrial revolution, signifies a new era in manufacturing and industrial operations marked by digital transformation and interconnectivity. It leverages advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), big data analytics, and cloud computing, blending the physical and digital worlds to create “smart factories” and “smart systems.” These integrated systems provide unprecedented levels of automation, efficiency, and data exchange in manufacturing and other industrial sectors.
A critical component of this digital transformation in Industry 4.0 is energy monitoring. With the ever-increasing need for industries to optimize their energy usage and reduce operational costs, the importance of energy monitoring cannot be overstated. Energy monitoring involves tracking, analyzing, and managing the energy consumption of an industrial system. This not only provides valuable insights into a system’s energy use but also helps identify areas of waste and inefficiency that can be targeted for improvement.
In the context of Industry 4.0, energy monitoring goes beyond just tracking energy usage. It utilizes advanced technologies to provide real-time data, enabling immediate responses and adjustments. It supports predictive maintenance by identifying anomalies in energy consumption that may indicate potential equipment failure. Furthermore, it contributes to data-driven decision-making, helping to align energy management strategies with broader business goals.
As industries worldwide strive to achieve sustainability goals, comply with energy regulations, and improve their bottom line, energy monitoring plays a critical role. This article will delve into the concept of energy monitoring in the context of Industry 4.0, discussing its role and advantages. The intention is to highlight its significance in driving energy efficiency, operational effectiveness, and sustainability in the era of digital industrial transformation.
Energy monitoring, in its most basic form, is the process of tracking, analyzing, and controlling the energy consumption of a system. It involves the use of meters or sensors to collect data about energy usage, which is then analyzed to gain insights into energy consumption patterns. This information is in turn used to help identify inefficiencies and optimize energy usage.
In the context of Industry 4.0, energy monitoring takes on new dimensions. It doesn’t just involve tracking energy consumption but leveraging advanced technologies to gather and analyze data in real time, creating a comprehensive and instantaneous picture of a system’s energy usage.
One of the defining characteristics of Industry 4.0 is the integration of physical and digital systems. In terms of energy monitoring, this means the integration of energy meters and sensors with digital technologies like IoT, cloud computing, and AI.
IoT devices are used to collect data about energy usage, which is then sent to a cloud-based system for storage and analysis. These intelligent sensors have the capability to measure a number of different parameters and send them to the cloud for analysis. This is required as the data set is quite large and it needs a lot of computing power in order for the AI algorithms to process it.
AI and Machine Learning (ML) algorithms that are applied to the data are looking to identify patterns and anomalies. This can provide valuable insights into how energy is being used and where inefficiencies may lie, or even prevent issues with equipment as the heightened energy usage can indicate impending equipment failure. AI can also help to predict future energy usage based on past data, aiding in planning and decision-making processes from a more long-term perspective.
In order for the obtained insight from the data and its analysis to be useful it needs to be presented efficiently so it can be ingested in an optimal manner by a human being (the one making the decisions in most cases). This necessitates the development and use of advanced visualization tools for the purposes of these new Industry 4.0 Energy monitoring systems.
Overall, energy monitoring in Industry 4.0 is about much more than just keeping track of how much energy is being used, which has been the norm so far in the domestic and industrial sector. It’s about using advanced technologies to collect, analyze, and act upon energy data, leading to more efficient and sustainable industrial operations. It gives you the power to make the right changes in a system to bring it from a poorly performing to an optimally working one.
The following chapters will look deeper into what Advanced Energy monitoring in the context of Industry 4.0 is and present more information on the specifics of how it operates, what strategies are there to implement it, what are some good examples of IoT Sensor devices to utilize and what platforms for data processing and analysis exist that one can use.
Energy monitoring plays a pivotal role in Industry 4.0, acting as a cornerstone for operational efficiency, sustainability, and cost-effectiveness. The integration of energy monitoring with other Industry 4.0 technologies like IoT, AI, and data analytics brings forth a new realm of possibilities, not just for energy management, but for overall industrial operations. The combination of the aforementioned components is what makes the difference between conventional and advanced energy monitoring.
Let’s look at the main components that need to exist in order for an energy monitoring system to be counted as a smart solution for optimizing power consumption, not just a metering one.
These are the building blocks, they are essential for a good energy monitoring solution. If you want to make good decisions, based on advanced analysis your data needs to match. High-quality data that is accurate, void of measurement errors and has sufficient granularity is what separates a good quality sensor from a mediocre one.
Some of the more important parameters one needs to factor in when selecting the energy monitoring sensors are:
If you are interested in a sensor that covered all of the aforementioned, you should take a look at the NCD 3 Channel Industrial IoT Wireless AC Current Monitor Sensor. It is a good representation of a high-grade, industrial-level sensor for current monitoring should provide in terms of functionality and build quality.
This is important mainly for a time and cost-savings perspective. In order for the data to get to the platform where the AI analytics magic is happening the sensors need to be connected over a local network that transfers the packets to your cloud (or local server). In the days before Industry 4.0 this might have been done via cables that power and/or carry the measurement data for example using a Canbus or Modbus system.
Modern systems utilize wireless solutions specifically designed for industrial applications (think something similar to Wi-Fi, however specifically designed to circumvent its range and power consumption shortcomings). Some of the more popular players in the field are DigiMesh, LoRaWAN, Nb-IoT. All of the aforementioned provide long range (some better than others – LoRaWAN, DigiMesh), good battery life (LoRaWAN and DigiMesh can reach up to 10 years), and a high level of security via packet encryption.
There is no shortage of end-devices utilizing the aforementioned communication technologies that provide a good selection of onboard sensors (like the NCD DigiMesh device we referenced above) and they are getting more and more cost-efficient. At this point, they have no negatives compared to a wired sensor, evidence of which is the fact that such devices exist that convert traditional wired sensors to wireless ones (for example RS485 to LoRaWAN converters).
Getting good quality data is important, so is having an efficient method to communicate it from the sensor to the platform that will evaluate it. The next step is to select the right platform.
There are many solutions, most of which exist in the Cloud (due to the advantages in security and reliability they provide), that one can use to host their analytics algorithms. Depending on the sensor selected you could have a very easy time integrating the data, or a very costly (in terms of time and human effort) one. Look for a sensor that has good integrations out of the box with the most popular platforms and has extensive API documentation.
If you are looking for open platforms, check out Node-RED as it provides a user-friendly visual way to manipulate data. It is easy to use as it requires minimal code in order to be configured and it has a wide range of functionality. It can easily parse data, reformat it and/or re-packet it to be pushed to an endpoint (for example utilizing MQTT or HTTPS). This couples nicely with InfluxDB which is a popular solution for keeping time-series data (which energy measurements are) that has a huge user base and also requires no license to use.
To top it all off and complete the chain one could use Grafana to extract and visualize the data from the database, this is great for real-time visualization and can also be used post-processing, after the ML algorithm has synthesized some new metrics.
All of the aforementioned come nicely packeted in a complete stack called MING (MQTT, InfluxDB, Node-RED, Grafana) and a very user-friendly guide on how to implement it has been provided in the Balena blog.
If one would want to go the more commercial route, the main players in the game are AWS IoT Core and Azure IoT Hub. They provide complete solutions that don’t require 3rd party integrations and can take care of your entire infrastructure. Your data will be processed, stored, and can be manipulated appropriately. They have very detailed documentation and it should not be hard to create a solution based on their services.
The backbone of the analysis are the ML algorithms employed. Getting good sensors and a solid platform to host your services are the founding steps, however, if you are not able to make sense of the information you worked so hard to provision, you won’t be learning much, thus your decision-making will not improve. The particularity of what algorithms to use is very complex and out of the scope of this article, It is worth noting that as you are working with real-time data, they should be speedy. This will ensure actions can be taken on time.
As a matter of fact, AI and ML are precisely what makes the difference between a traditional preventive maintenance system and a predictive maintenance one (this is the core of Industry 4.0). Analyzing data, observing long-term system behavior, and making predictions on what will happen in the near and far future with a high degree of certainty, are the tools that enable good decision-making.
The implementation of energy monitoring within the framework of Industry 4.0 offers numerous advantages that go beyond simple energy conservation. As we have already provided a base understanding of what the components of the system are (in the previous chapter), let us move on to discussing in more detail the edge one is given by introducing Advanced Energy Monitoring in the ecosystem. The following are some key improvements one can take advantage of.
By providing real-time, detailed data on energy consumption, energy monitoring allows for the identification of energy inefficiencies and wasteful practices. Industry 4.0 energy monitoring systems can pinpoint excessive energy usage at a granular level, enabling businesses to address these inefficiencies and substantially reduce energy usage, thereby improving their overall energy efficiency.
As we mentioned already, energy monitoring can serve as an early warning system for potential equipment failure. For example, an unexplained increase in energy usage could indicate a malfunctioning machine or system. Recognizing these signs early allows for timely maintenance or repairs, preventing costly and disruptive system failures and unplanned downtime.
Through real-time monitoring, energy consumption data can offer valuable insights into how different processes and operations affect energy use. This information can then be used to adjust operations to minimize energy usage, potentially leading to significant cost savings. For example, it may be possible to schedule energy-intensive operations for times when energy costs are lower.
With increasing pressure to reduce their carbon footprint, companies can use energy monitoring to help achieve their sustainability goals. By reducing energy use, companies not only save money but also contribute to environmental preservation. Demonstrating a commitment to energy conservation can also enhance a company’s reputation and social responsibility profile.
The extensive data collected by energy monitoring systems can inform decision-making at all levels of an organization. For example, it can help managers develop strategies to reduce energy usage or assist executives in aligning energy management with broader business goals. This data-driven approach can lead to more effective and strategic decisions.
In the context of Industry 4.0, energy monitoring systems can be integrated with other systems, such as manufacturing execution systems (MES) and building management systems (BMS). This integration allows for better coordination and management of various operations. Moreover, the use of AI and ML algorithms can automate responses to the data collected by energy monitoring systems. For instance, if a system detects high energy usage, it could automatically adjust equipment settings to reduce consumption.
With the enforcement of stricter energy conservation laws worldwide, energy monitoring can help industries ensure compliance with these regulations. Real-time monitoring of energy usage allows for quick detection and rectification of any non-compliance issues, thereby avoiding potential penalties and fines.
Perhaps one of the most significant advantages of energy monitoring in Industry 4.0 is the potential for cost reduction. By enabling industries to monitor, control, and optimize their energy usage, energy monitoring can lead to significant cost savings. The reduced energy costs, combined with savings from predictive maintenance and optimized operations, can contribute to a substantial reduction in operational costs.
In essence, energy monitoring in Industry 4.0 offers a host of benefits that can enhance the operational efficiency, sustainability, and cost-effectiveness of industrial operations. These advantages underline the critical role of energy monitoring in the digital transformation of industries.
In conclusion, energy monitoring is an indispensable part of Industry 4.0. It stands at the intersection of energy efficiency, operational optimization, predictive maintenance, and sustainability. The importance of energy monitoring in Industry 4.0 cannot be understated as it plays a pivotal role in propelling industries toward a future marked by higher efficiency, lower costs, and enhanced sustainability. As such, industries worldwide must embrace energy monitoring and integrate it into their digital transformation strategies to stay competitive, sustainable, and future-ready.