However, there are three reasons why we’re interested in assessing the exact energy use of a specific piece of equipment.
Therefore, today we’ll cover what these are and how you can successfully measure them without becoming too technical:
Today's Lesson: Measuring Energy Consumption
How to quantify electricity use effectively.
Number Of The Day
Gumapas et al. published a study investigating the energy consumption of their freezers. They found that each 1 °C increase in room temperature led to an extra 18 kWh of energy use per month, releasing an additional 9.27 kg of CO₂e from their models. Catching these and other nuances is essential, and that’s why we’ll show you how to do it:
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How To Measure Yourself
Few researchers have a clear sense of how much energy a particular piece of equipment consumes.
I compiled a few interesting numbers for you here - please note that differences among manufacturers can vary widely and most impacts shown here are average ranges of newer models.
Moreover, measuring energy consumption often sounds complicated—something only experts can handle. Nothing could be further from the truth.
Here is how anyone can do it—and why it’s so beneficial.
Why Measuring Is Valuable
Measuring the energy consumption of your lab allows you to:
Calculate the exact footprint of your laboratory
Identify where to prioritise efforts to enable savings
Decide when to replace an older instrument
In some parts of the world, grid limitations are already preventing institutions from adopting new instruments, so accurate data is becoming essential.
This graph is from a study conducted on the campus of the University of Stanford showing how much energy lab equipment requires – averaged throughout multiple laboratories.
Caveats—and How to Avoid Them
In theory, measuring energy use is straightforward, but a few mis-steps can compromise an entire dataset. Let’s look at how to get it right.
Outline Your Plan
First, decide between two main approaches:
Estimation Take the average energy consumption listed by the manufacturer and multiply it by the time your equipment is in use.
Works well for devices that run 24/7 (e.g., freezers)
Less accurate for devices with variable settings (e.g., PCR cyclers, centrifuges)
Especially in dry labs, server costs (including those of service providers) can have a significant impact. Luckily, there are several tools that help you estimate your impact such as Green Algorithms, CodeCarbonor CarbonR. This data also comes from an academic institution.
Check the ACT label—or the manufacturer’s specs—for consumption numbers. For one week, ask users to time their sessions or note settings in a lab book or shared spreadsheet; this gives you realistic usage hours.
Measurement Use meters to record actual energy use:
Plug-in energy meters sit between the instrument and the outlet—easy to obtain and operate.
Clamp meters (hand-held or panel-mounted) measure current on hard-wired circuits and can log detailed data.
Need more on clamp meters? See the guide in our free Slack channel. Below, we’ll focus on plug-in meters because they’re simplest to deploy.
Setting Up a Protocol
Ask yourself exactly what you want to measure, otherwise the data may be of limited value. Decide whether you aim to:
Compare two processes (e.g., Protocol 1 vs Protocol 2), or
Produce a broader assessment (e.g., annual lab energy use)
If you merely note the total energy of a single machine, results can mislead because:
Freezer energy varies with door openings and load.
PCR energy depends on settings and daily run count.
Centrifuges might be used seven times one day and only once the next.
Farley and colleagues showed that opening a freezer door for just 1 minute can lead to significant temperature drops. However, the stark variation in data comes from temperature differences from measuring bottom vs middle vs top shelf within the freezer. Of note, how severe the differences are depends on the model.
Define Your Setup
Goal – What question are you answering?
Assumptions & circumstances – Protocol details, room temperature, layout, etc.
Method – How data will be collected and which colleagues are involved
Example 1 Compare: five centrifuge runs at 5 000 rpm for 10 min at 4 °C (with one fast cool-down) versus five runs at 10 000 rpm for 5 min at 4 °C (with one fast cool-down)
Example 2 Measure freezer energy for three weeks in February and again in June to estimate average annual consumption.
Taking Notes
Document well. Here are factors that are often forgotten:
Instrument model and age
Load or settings during measurement
Type of energy meter used
Environmental factors (e.g., room temperature)
Data-logging frequency (raw data availability)
Tip: Record your reasoning too—details that seem trivial now can explain results later.
Applying The Knowledge
You have put a lot of effort into your undertaking, don't forget to reap the benefits - share your data!
After measuring, you can project costs or quantify savings—powerful evidence when persuading colleagues or much valued support for the community to help them drive their cause.
Don’t be surprised if your numbers differ from the supplier’s; manufacturers follow strict test protocols that rarely match real-world conditions.
Factors such as ice buildup are hard to quantify and therefore, are easily ignored or overlooked during assessments.
Remember, many factors affect energy use: freezer ice build-up, instrument age, room placement, ambient temperature, and more.
Whenever you read someone else’s report, check their setup and ask how they derived their figures before applying them to your own situation.
Upcoming Lesson:
Choosing More Sustainable Enzymes
How We Feel Today
References
Gumapas, L. A. M. et al., Factors affecting the performance, energy consumption, and carbon footprint for ultra low temperature freezers: case study at the National Institutes of Health. 2012. World Review of Science, Technology and Sustainable Development, 10(1–3). doi:10.1504/WRSTSD.2013.050786
Hafer, M. et al., Quantity and electricity consumption of plug load equipment on a university campus. 2017. Energy Efficiency, 10:1013–1039. doi:10.1007/s12053-016-9503-2
Lannelongue, L. et al., Green Algorithms: Quantifying the carbon footprint of computation. 2021. Advanced Science. doi:10.1002/advs.202100707
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