Energy managers are not short of data. Half-hourly meter readings, BMS data, tariffs, weather data and equipment schedules can all contribute to a better understanding of how a building is performing. The challenge is what happens next.
By Satish Swaroop, President & CEO, CEBS Worldwide.
A dashboard can tell us that demand reached a particular level at 7:00 pm, that consumption increased compared with the previous month, or that a building has a relatively poor load factor. All of this is useful. But none of it, by itself, answers the question an energy manager ultimately has to deal with: what should we change?
That distinction between monitoring and decision-making is becoming increasingly important.
Consider a building with a pronounced evening peak. The first response should not necessarily be to invest in new equipment. We need to understand what is contributing to the peak, whether it is persistent and whether there are operational changes that could address it.
Could plant schedules be adjusted? Is heating or cooling starting too early? Are different systems operating simultaneously when they do not need to? Would on-site generation help? Could battery storage reduce peak grid demand?
These are quite different questions from simply asking how much energy the building consumed.
From observation to investigation
This is where an Operational Energy Digital Twin can be useful.
The purpose of a Digital Twin should not be to create a more elaborate dashboard or an impressive three-dimensional representation of a building. The value comes from connecting available energy evidence with property and operational context and then providing a controlled environment in which potential changes can be investigated.
Take rooftop solar as an example. Identifying suitable roof area and estimating annual generation is useful, but it is only the beginning. The more interesting question is how that generation aligns with the building’s demand profile.
Battery storage introduces another set of considerations. When would the battery charge? When should it discharge? Is the objective to reduce peak demand, increase the use of on-site solar, respond to tariffs, or achieve a combination of these?
A Digital Twin allows these alternatives to be considered against a common baseline before investment is committed.
The same principle applies to less capital-intensive measures. Changing an operating schedule may cost very little compared with installing new technology, yet its effect can still be tested alongside solar, storage or other interventions.
Test the decision, not just the technology
This changes the role of the energy model.
Instead of producing a single forecast or savings figure, it becomes a way of asking structured “what if?” questions.
What happens if operating hours change? What happens during winter rather than summer? What if solar capacity is increased? What if storage is added? What if two measures are implemented together?
The objective is not to predict the future with absolute certainty. It is to compare reasonable alternatives using a consistent set of assumptions and evidence.
A Digital Twin does not compensate for poor data. Where evidence is incomplete, that limitation should be visible rather than hidden behind an apparently precise model.
There is an important discipline required here. Measured, derived and modelled information are not the same thing.
If an electricity value comes directly from a meter, it should be identified as measured. If a demand profile has been calculated from available evidence, that should be clear. If solar generation or battery behaviour is simulated, the resulting values are modelled outcomes, not measured savings.
This may sound like a small distinction, but it matters when the results begin to influence investment decisions.
A polished visualisation should never create more confidence than the underlying evidence justifies.
Moving towards decision support
We have applied these principles in developing ENERGE TWIN, an Operational Energy Digital Twin developed by CEBS Worldwide. One lesson from that work is that the most useful question is rarely: “How much energy did we use?”
The more valuable questions are usually: Where should we look? What could we change? What might happen if we changed it? And how strong is the evidence supporting that conclusion?
Energy dashboards will continue to have an important role. But as buildings become more connected and energy decisions become more complex, there is an opportunity to move beyond monitoring towards structured decision support.
Before making the change – particularly an expensive one – it makes sense to test the decision first.
This article appeared in the September 2026 issue of Energy Manager magazine. Subscribe here.
