4 Industrial Examples of IoT Energy Management

Industrial IoT Energy Management

70% of global electricity is consumed by industrial machines. And that costs a fortune. Especially if you know that many of these machines operate inefficiently, using more power than they need to. The good news for your business is that you can still optimize your processes, improving efficiency, cutting costs, and becoming greener as a result. These are the benefits industrial IoT energy management could bring. As proof, take a look at the four promising use cases below.

Industrial IoT Energy Management Scenarios

The Industry 4.0 era is an opportunity to make production more efficient. For example, manufacturing and mining companies can improve their energy use with multiple industrial manufacturing solutions. These solutions allow them to predict how much energy they will need and then find ways to reduce it. This optimization may have the following two levels or stages:

  1. It all starts with sensor-based monitoring. Various types of sensors gather data on velocity, temperature, and humidity from energy equipment and send it to the cloud in real time, revealing the full picture of energy use. This allows energy providers to track energy spikes and voltage changes remotely in addition to watching asset health and planning repairs. Meanwhile, energy users can plan their power consumption and machine use more wisely. Overall, IoT monitoring helps both providers and users understand the data the sensors collect and use it to make decisions.
  2. Artificial intelligence advances sensor-based monitoring. Algorithms process the collected data, come up with insights, and make predictions. Energy providers can use AI to forecast breakdowns, while AI-powered software provides insights on how to avoid incidents. For energy users, AI-powered software enables them to predict future energy needs and reduce waste. An overall advantage is that an AI system can be smart enough to make decisions in place of human workers.

You can go further and build even more sophisticated IoT-based solutions by utilizing other tech advances. Here are some examples:

  1. Computer vision cameras enable companies to watch equipment 24/7 and reveal failures faster than human workers.
  2. Self-piloted drones fitted with cameras make it possible to access hazardous or remote sites and monitor mining equipment or power lines.
  3. Digital twin models, besides basic monitoring, make it easy to simulate a variety of optimization scenarios and show potential outcomes.

From a market perspective, managing energy with IoT is both necessary and on trend. Businesses have to respond to rising concerns about carbon footprints. IoT energy monitoring shows a more resilient and sustainable path to energy use, so it comes as no surprise that in the upcoming years, energy management will increase.

industrial iot energy management

Let’s take a look at the most promising application areas of industrial IoT energy management.

Use Case 1. Regulating Air Flow with Ventilation on Demand

In the mining industry, something as simple as air can cost a fortune. Underground ventilation accounts for up to 40% of the total energy consumption in a mine. At the same time, it’s crucial to ventilate mines properly. Poor air quality due to emissions from equipment makes them dangerous for humans. This is where the ventilation-on-demand (VoD) system comes into play.

The VoD system aims to deliver the right air quality and quantity where and when it’s needed. This is achieved by locating personnel and equipment in the mine with tag and tracking systems. Sensors also measure the airflow volumes through the mine and send this data to the VoD system. Then, the system adjusts ventilation in areas where personnel and equipment are present. It’s also possible to schedule airflow in different parts of the mine.

While traditional systems ventilate the entire mine, including areas that don’t function, the VoD system can direct air only where and when it’s needed. Such optimization results in much lower electricity use for mining companies. It also helps reduce CO2 emissions.

Real-Life Example

Newmont Corporation is the world’s largest gold mining corporation. They wanted to boost productivity and reduce energy costs in their Eleonore gold mine in Canada. Howden, a manufacturer of air and gas handling solutions, helped them equip their mine with a VoD system.

The solution is an automated system that covers all the ventilation equipment in the mine. There are 30 monitoring stations that define the quantity and quality of fresh air at various points underground. Each station has a flow sensor and gas sensors that monitor for hazardous gasses.

Workers and vehicles have RFID tags indicating their positions in the mine. This data is sent to a mine-wide tracking system. Then, the VoD software analyzes it to ensure that each zone with people receives enough fresh air.

With the VoD system, Newmont Corporation reduced electricity costs for underground ventilation by half and surface ventilation by 73%. The system also enabled them to improve working conditions for their employees, as all workers now have the optimal supply of fresh air.


  • Cutting electricity costs by half
  • Meeting regulations to reduce CO2 emissions
  • Making the workplace safe and comfortable for workers

Use Case 2. Technical Maintenance with Digital Twins

The technical maintenance of industrial machines consumes significant energy. Consider the tech adjustments needed for mining haul truck valves. When companies rely on manual labor, workers have to visually inspect all the valves to adjust those that need it. However, this process is much faster with sensors. Sensors attached to the equipment only flag the valves that need adjustments. For mines, this saves time and money. The sensor system allows companies to create virtual models of their equipment and watch its health in real time.

Digital twin models are based on data from motion, temperature, and spatial sensors. These sensors are attached to physical units and send data to virtual replicas, enabling ML-based software to recognize anomalies quickly. (It’s also possible to augment the solution with historical data on failures.) Operators are notified about potential issues on the spot. What’s more, digital twins predict when maintenance is needed, so companies can see problems before they occur and predict future outcomes.

Real-Life Example

Rolls-Royce powers more than 35 types of commercial aircraft and has 13,000 engines in service around the world. With digital twins, they can watch how each engine operates and schedule maintenance or repair. Rolls-Royce engineers have installed onboard sensors on each engine and created its digital twin. The twin operates in the virtual world as the engine functions, enabling the company to monitor its health and environmental conditions.

Digital twins have helped Rolls-Royce make their engines more efficient. In some cases, the time between maintenance has increased by up to 50%. The company has also reduced their inventory of parts and spares. Plus, less preventative engine maintenance means reduced aircraft downtime.


  • Needing to maintain equipment less often
  • Reducing money spent on maintaining equipment by a third
  • Saving energy on preventative maintenance services

Use Case 3. Watching Equipment with Computer Vision

Manual inspection is difficult, time-consuming, and expensive. Operators have to look through reams of visual data to find defects and label them. This is a monotonous task that can cause fatigue, and some defects can be omitted or misclassified as a result. Human involvement puts machines on the factory floor at risk of not functioning properly. Taking into account that some machines may need to work non-stop, getting them restarted can cost a fortune. These are just a few situations when the human eye can’t compete with computer vision capabilities.

Computer vision (CV) is an advanced subset of AI that can interpret visual data. It can detect objects, classify them, and find their locations on pictures. With integrated cameras, CV allows companies in the energy sector to monitor the state of pipes and cables and check if the equipment needs utility services. CV algorithms can also recognize smoke or fire on devices or power lines.

Importantly, CV cameras have minimal impact on the existing infrastructure and can cover large, disturbed areas.

Real-Life Example

Aramco is a Saudi Arabian petroleum and natural gas company. They wanted to monitor their equipment to prevent failures that cause blowouts on drilling rigs. With the help of FogHorn, they deployed an edge-based CV solution at their factory.

The system is based on “smart” high-bandwidth cameras that monitor drilling rigs. The data from cameras is analyzed in real time using the edge technology, while camera-based edge analytics detect the slightest signs of potential failures. There is extremely low latency, and workers can see alerts on a dashboard and shut down a drilling operation if a well blowout is on-coming.

Aramco also added AI to their surveillance system, so it can now detect fixed and moving assets and workers. The solution sends an automatic alert if workers violate the safety guidelines or a hazard occurs.


  • Helping engineers detect equipment failures
  • Saving money on equipment repairs
  • Improving safety compliance on the shop floor
Looking for a development partner to create a CV solution for your shop floor? Turn to Softeq and ideate together!

Use Case 4. Monitoring Hazardous and Remote Areas with Drones

Oil and gas companies have lots of areas to monitor. These include drilling rigs, oil wells, and refineries plus remote offshore platforms and miles of pipelines. Monitoring these areas can be risky, and they might be inaccessible for human workers. It’s crucial that the equipment in these areas is monitored to avoid costly downtime and incidents and their associated energy loss.

This is where drones come in handy. Fitted with cameras, drones can take photos and videos from almost any angle. ML-powered software then processes the visuals and makes sense of disparate inspection data. All this helps reveal defects better than humans can and means oil and gas companies can reduce their operational costs.

Real-Life Example

The Beauly to Denny power line in Scotland includes 615 transmission towers over a distance of 220 km. Drones are used to watch all this equipment. During the inspection, a two-person team—a camera operator and drone operator—moves from site to site. The drone operator pilots the visual line of sight drone while the camera operator photographs tower components. Processing software then grades each component against the defect standard and the results appear on a visual asset management platform.


  • Safer and more efficient monitoring of hazardous areas
  • More reliable and stable energy supply
  • Additional data sources and insights on energy consumption

Bottom Line

The industrial sector has a high energy demand. However, energy can be saved when old technologies are replaced with new ones.

With IoT energy management, companies can automatically control energy supply on the shop floor and quickly respond to changing energy needs and prices. Tech advances like computer vision or drone-based inspections and digital twin-based monitoring can contribute to this. As a result, you can:

  • Save money on equipment repairs and maintenance
  • Reduce the risk of volatile energy prices and supply
  • Ensure compliance with current and future regulations
  • Stay competitive in the global arena

Softeq can help you make the process of digital transformation smooth, effective, and affordable.