Real-time energy data is no longer just nice to have. It has become a must-have.
If you are working in the field of energy conservation or preventive maintenance, real-time energy data is a paradigm shift. Even hourly measurements are outdated now that load peaks are calculated on the basis of the highest consumption in a single hour. What would your service look like if Smart EO was the foundation you built it on?
Whether your need is control, visualisation, warning flags, specialist tools for a particular type of installation or top system, what doors could you open with access to real-time energy data? Imagine if you could turn on the ventilation ten minutes before the air in the meeting room became unbearably stale! Not that we measure air quality by means of energy data but, if the ventilation system is drawing zero electricity, you can expect the air quality to become poor in a fairly short space of time.
What is real-time energy data?
Real-time energy data means that you can see changes as they happen. But what does real-time energy data mean for you? Does it mean sampling every second? Every minute? For us, it depends entirely on the area of application. Every minute, with a two-minute lag, is real-time enough to avoid load peaks, as long as load peaks are average peaks per hour.
For an operator standing, iPad in hand, wanting to see the changes there and then, a minute can be a long time to wait. In such a situation, you may want to go for sampling every ten seconds. With our system, you can choose precisely the level of resolution you need, but we believe the standard should be as high a frequency as possible. There is no point driving at 40 kph on a motorway with a 100 kph speed limit if traffic is flowing smoothly, is there?
With real-time data, you can see abnormal behaviour. For example, if you experience a lot of inefficiency within a single hour, this will not be apparent
with hourly sampling. With real-time data, on the other hand, it will be discovered. This lets you be a better advisor to your customers and helps to even out and optimise their energy consumption. When your customer can see the effects in real time, you can map out where making changes can provide the biggest gains.
We see two particular challenges that real-time data resolves:
1. Load peaks
Sporadic load peaks are inefficient and expensive for your customers. If there are only a few hours in the month when the customer needs to draw a lot of power all at once, they could end up paying far too high a grid charge for a sporadic need.
For example: The grid charge is calculated on the basis of the hour in the month when the load is highest. If you used most power at 13:30 on Monday, 5 May, that hour sets the price for the entire month.
With real-time energy data, load peaks can be evened out and checked for. If your customer also has real-time logging at several measuring points, they will get the benefit of in-depth data as well. This allows load peaks to be flagged and prevented before they happen.
Load peaks, by their very nature, rarely repeat at entirely regular intervals. The biggest bang often comes when the driveway snow-melting cables have been turned on, everyone is using the lift, all the electric vehicle chargers are in use and the neighbourhood children’s Christmas party is in full swing at the same time. With real-time energy data, you can see not only that a load peak is approaching but also why it is happening – allowing you to do something about it.
By spreading electricity consumption evenly throughout the day, your customer will avoid having to pay higher than necessary power transmission charges. By controlling or displacing some consumption, peaks can be evened out, which will have a major impact on electricity bills.
2. Operational inefficiency
Operational inefficiency is the second challenge that real-time energy data can solve. If a building’s overall operations are going to be automated, you are dependent on using energy data that enables systems to be run at the right time. In which case, you must have high-resolution energy data that arrives immediately. Take ventilation, for example. Can real-time data give your customer better tools to optimise a building’s air conditioning system?
One thing we typically see in buildings is that systems are run at the wrong time. For example, that the heating and cooling systems run concurrently. Even though that may only happen in a transitional period, with both operating for only half an hour at a time, the excess costs will mount up. It is extremely inefficient and expensive as well.
With access to real-time energy data, an operator will be able to eliminate such inefficiencies. They will also be in a better position when they are actually in the building making adjustments. The results will immediately be apparent in real time. It will also be possible to monitor the consequences of any changes, and settings can automatically be optimised to avoid inefficiencies.