| Written by Constance Stickler
Energy costs impact the cold chain
Crisp salad, plump berries and cheese with complex flavours. That is what we want to be offered in the store - and what reefers make possible. Without them, refrigerated transport would be very limited. But while consumers enjoy the conveniences of the cold chain, the shipping industry feels a chill going down its spine: these vital containers' ever-increasing energy demand.
Operating the indispensable refrigerated containers involves considerable energy costs. The units' cooling systems require a lot of electricity to maintain constant temperatures, especially under extreme outdoor conditions. The situation is becoming more acute at container terminals: connecting a large number of reefers at the same time requires peak performance from the power grid and drives up costs.
High energy costs have a direct impact on the profitability of reefer logistics. Terminal operators, shipping companies and end users all bear the financial burden of maintaining temperature stability. In addition, the environmental impact of greenhouse gas emissions is immense, complicating efforts to build a more sustainable supply chain.
With energy prices rising and environmental regulations to be complied with, it is becoming increasingly urgent to find ways to reduce reefers' energy consumption effectively. IoT technology represents a promising solution that can optimise energy consumption.
Why does a reefer unit consume so much energy?
Reefers' energy requirements are so significant because maintaining stable temperatures and other conditions such as optimum humidity requires the refrigeration components to be running almost continuously. The more the ambient temperature deviates from that specified for the goods being transported, the more the refrigeration system is challenged.
Older models tend to consume more energy due to less efficient compressors, outdated insulation materials and limited control over refrigeration cycles. In contrast, newer models incorporate advanced technologies such as variable speed compressors, energy-efficient fans and improved insulation, which significantly reduce energy consumption. They often consume 20 to 40% less than units manufactured just a decade ago.
To put energy consumption in perspective, a single reefer consumes about 4 to 5.8 kW per hour, or 96 to 139.2 kWh per day, depending on size and factors such as ambient temperature. (1)
For comparison - if the following devices were also left running for 24 hours, they would consume about the same amount of electricity on average:
- Household refrigerator: 2.4 kWh
- Modern desktop PC: 2.4 kWh
- TV: 4 kWh
- Vacuum cleaner: 12 kWh
- Hair dryer: 57.6 kWh
- Central air conditioner: 72-120 kWh
An average household in the USA consumes 28.77 of electricity per day (2); also, here, cooling (air conditioning) is often the most important factor (up to 17,5%).
Traditional monitoring methods fall short
Traditional monitoring methods for refrigeration units rely heavily on manual processes and regular checks, which are not enough to ensure optimal performance. More or less regular inspections capture temperature settings, energy consumption and equipment functionality as a snapshot. This approach is more reactive than proactive: problems are often not detected until they cause inefficiencies or have already caused cargo spoilage.
The major shortcoming is the lack of continuous real-time data. Without continuous monitoring, detecting deviations in time is often a matter of luck. Compressor failures or a door left ajar instead of closed by mistake can go unnoticed for hours and, in addition to affecting the quality of the cargo, also lead to wasted energy.
There is also a lack of integration and scalability. If the data is not at least transferred to a central system, it remains isolated information that is not suitable for identifying systemic inefficiencies and problems in a fleet. Manual logging and insertion into the system also carries the risk of human error and reduces the reliability of the information.
These disadvantages impair energy efficiency and affect operational decisions that lack effective foresight. If inefficiencies are identified in such a scenario, the cost of corrective measures is often already high. The lack of analysis and forecasting functions significantly limits the possibilities for optimising energy consumption, so these monitoring methods are increasingly inadequate given today's challenges and foreseeable future developments.
The IoT extends to a reefer unit
The Internet of Things (IoT) is a network of interconnected devices equipped with sensors, software and communication technologies that enable data exchange and automation. IoT can transform reefers into intelligent and connected devices and enable real-time monitoring, diagnostics and optimisation. The integration of IoT, therefore, leads to unprecedented transparency and control over operating parameters.
IoT applications in reefers include remote temperature monitoring, energy efficiency management and predictive maintenance. The sensors record temperature, humidity, and other conditions, but they can also indicate door status (open/closed) and power consumption. They transmit the data to central platforms and can send alerts and alarms in case of deviations from the set parameters to enable quick corrective action, which immensely reduces the risks to the cargo.
However, the benefits of IoT-supported cold chain operations are not only in the real-time monitoring of conditions at present. In addition, they facilitate advanced analysis of historical information and enable data-based decision-making, for example by identifying patterns. For example, machine learning and big data can predict when maintenance work is required, preventing costly breakdowns and energy waste.
How can IoT-enabled sensors monitor reefer performance?
IoT-enabled sensors act as the eyes and ears of reefers, collecting real-time data on critical performance metrics. These sensors include:
- Temperature sensors: Monitor internal temperature to ensure it stays within predefined thresholds.
- Humidity sensors: Measure moisture levels to protect goods that are sensitive to changes in humidity.
- Energy consumption sensors: Track power consumption to identify inefficiencies and support energy conservation measures. They also indicate whether or not power is being maintained - a non-trivial underlying issue with hundreds or even thousands of reefers on-site at the same time.
- Vibration and shock sensors: Detect handling impacts that could affect not only the integrity of the cargo but also the structure of the container.
- Door sensors: Record the opening and closing of doors, helping to identify potential security breaches or cases of energy loss.
What types of data can IoT collect from a reefer unit?
IoT technology enables refrigeration units to generate and collect a variety of data points, each critical to efficient and reliable operation. Key data types include:
- Temperature data: Continuous records of internal conditions to ensure perishable goods are stored at optimal temperatures.
- Energy usage data: Insights into power usage patterns, identifying peak usage times and savings opportunities.
- Environmental data: External temperature, humidity and pressure readings that affect refrigeration performance.
- Performance metrics: Refrigeration cycle data, compressor status and fan speed data that help monitor unit health.
- Operational events: Logs of door openings, location tracking and hours of use that enable comprehensive audits of refrigeration activity.
How do IoT solutions transform raw data into valuable insights?
The data collected by the sensors is transmitted to central platforms via wireless networks. Operators can access the data via dashboards or mobile apps and monitor refrigerated containers remotely from anywhere. Advanced systems also provide real-time alerts that inform operators of anomalies such as temperature spikes, power outages, or unauthorised door openings. Another advantage is that changes to the settings can be made remotely.
A combination of temperature and energy data can identify inefficiencies in the cooling process, which can then be corrected immediately. Power supply problems can also be uncovered and addressed.
The insights derived from the data can also be used for predictive maintenance. Vibration sensors, for example, can detect early signs of engine wear and initiate maintenance work before a failure occurs. This prevents downtime and extends the life of the cooling units.
Compliance with international legal regulations is particularly important in refrigerated transport. In industries such as pharmaceuticals, detailed records of temperature and humidity conditions are required to demonstrate compliance with standards such as Good Distribution Practice (GDP). Automated data collection and storage simplify this process and reduce operators' administrative burden.
Aggregated data from all reefers on the terminal reveals trends and anomalies much more clearly than the sampled information from individual units alone ever could. This enables effective measures and processes to be implemented around energy consumption and demand fluctuation. The real-time information enables operators to make immediate adjustments to reduce peak demand, for example, through "peak-shaving".
The latter can be implemented by, among other things, dynamic adjustments to demand and price signals: cooling systems of certain containers are activated earlier to pre-cool them before the electricity price reaches its highest level at peak times.
IoT sensors connected to smart grids can collect real-time data on energy consumption patterns, allowing energy consumption to be distributed over different times of the day to avoid consumption peaks.
How does predictive maintenance save energy?
To perform predictive maintenance, IoT sensors must continuously monitor the cooling units' performance. Key metrics such as compressor operation, coolant levels, and fan speed are tracked in real time, allowing operators to identify and address early signs of wear. For example, a vibration sensor can signal an impending engine failure so that a timely repair can be made.
Struggling and underperforming equipment sometimes consume more energy than necessary. When the machines are running smoothly, energy consumption is more likely to remain constant and therefore predictable. It also reduces downtime and extends the operating life of the equipment.
Real-time monitoring optimising energy usage
Real-time monitoring is a vital cornerstone of IoT-driven energy optimisation. The continuously measured parameters are passed on to operators so that they can immediately identify and correct inefficiencies. For example, a sudden temperature deviation can indicate a defective compressor or poor insulation, which can be immediately remedied to prevent further energy loss.
Furthermore, the real-time data supports dynamic load management so that terminals can better plan and distribute energy-intensive activities. This also relieves the burden on local power grids, which have to cope with peak loads from multiple consumers. Benchmarking and trend analyses provide a comprehensive picture of developments towards systematic improvements in energy efficiency.
Machine learning and AI enhance IoT for reefers
Machine learning (ML) and artificial intelligence (AI) amplify the benefits of IoT in refrigerated containers by providing predictive and prescriptive insights. ML algorithms analyse vast amounts of sensor data to identify patterns, detect anomalies, and predict equipment failures. For example, a sudden increase in energy consumption can be interpreted as an indication of a deteriorating compressor, leading to preventive measures.
They can also optimise energy consumption by dynamically adjusting settings based on external conditions, cargo requirements, and historical performance data. This means energy is used efficiently without compromising the quality of the goods being transported.
FAQ
What is peak shaving in the context of container terminals?
Peak shaving at container terminals is a strategy for controlling energy demand during periods of high electricity consumption. The approach helps terminals reduce their operating costs and environmental impact while maintaining the necessary efficiency. Intelligent energy use prevents unnecessary overloading of the power grids and optimises the use of resources.
A common method of peak capping is to schedule energy-intensive activities during off-peak times when electricity demand is lower. This works well; for example, when electric vehicles are charged during the night, you may even be able to benefit from even lower electricity tariffs.
With reefer units, times when demand is lower can be used to pre-cool empty units. However, if a large number of loaded refrigerated containers arrive at the terminal at the same time, they must be reconnected within a certain time otherwise the cargo is at risk. Nevertheless, there is a certain amount of flexibility here: if the containers are monitored in real-time and all information, such as the period without connection, current temperature and other information, is available, the reefers can also be connected gradually - and not all at once. With electricity consumption monitoring, also in real-time, you can prevent going over the critical value, which would put you at a new electricity price level.
Takeaway
The Internet of Things is transforming reefer unit operations, offering a powerful solution to address the dual challenge of rising energy costs and environmental impact. Through real-time monitoring, predictive maintenance and AI-driven optimisation, IoT enables the identification of inefficiencies, prevention of costly failures and adaptation of energy consumption to dynamic conditions.
For terminal operators, this not only means significant cost savings but also improved operational efficiency and compliance with sustainability goals. With energy prices continuing to rise, integrating IoT solutions into refrigerated logistics is no longer optional – it is a strategic necessity for building a more resilient and environmentally friendly cold chain.
Delve deeper into one of our core topics: Refrigerated containers
Glossary
Big data refers to huge and complex datasets that traditional data processing software cannot effectively manage or analyse. It encompasses structured, unstructured, and semi-structured data from various sources, growing rapidly in volume, velocity, and variety. Big data requires specialised technologies and techniques to capture, store, distribute, manage, and analyse it, enabling organisations to uncover valuable insights and patterns. These insights can drive decision-making, improve operational efficiency, and create new opportunities across industries. (3)
Machine learning is a branch of artificial intelligence that enables computers to learn from data without explicit programming. It uses algorithms to detect patterns in large datasets, make predictions, and improve performance through experience. Machine learning applications span various industries, including healthcare, finance, and transportation. The technology powers image recognition, natural language processing, and autonomous vehicles, among others. As machines learn from data examples, they can adapt to new situations and solve complex problems more efficiently. (4)
Sources:
(1) https://www.gcs-reefer.com/en/sonnection-reefer-containers-power-grid
(2) https://www.eia.gov/energyexplained/use-of-energy/electricity-use-in-homes.php
(3) Big Data: Concepts, Technology and Architecture, Balamarugan Balusamy, Nandhini Abirami R, Seifedine Kadry, and Amir Gandomi, Wiley-VCH, 2021
(4) Foundations of Machine Learning, Mehryar Mohri et al., MIT Press, 2018
Author
Conny Stickler, Marketing Manager Logistics
Constance Stickler holds a master's degree in political science, German language and history. She spent most of her professional career as a project and marketing manager in different industries. Her passion is usability, and she's captivated by the potential of today's digital tools. They seem to unlock endless possibilities, each one more intriguing than the last. Constance writes about automation, sustainability and safety in maritime logistics.
