Near Real-Time Data:
What is it &
Why is it so Useful?

A look at near real-time data and some of the use cases relating to it.

23.01.2022

With the aftermath of the energy crisis still having an effect on us all, any relief that companies can offer their customers when it comes to their energy bills will be welcome. The right energy solution can alleviate some of the pain caused by the energy crisis and cost-of-living crisis. re.alto can save your customers money by determining their energy usage and enabling them to consume more intelligently. Access to near real-time data enables consumers and energy companies to both act and react and is therefore essential for successful energy management. Analysing this data greatly increases the capacity to create value for the consumer and to build more extensive energy services. Knowing what devices are consuming in real-time ultimately enables consumers to make data-driven decisions and provides utilities with the ability to personalise their services for a more customer-centric model, a potential that is yet to be fully realised in the energy sector. So, what exactly is real-time data and which use cases can be applied here?  

Real-time data refers to the elapsed time between when the data was generated and when it was transmitted for analysis. Real-time means the time interval is instantaneous, meaning the data needs to be streamed continuously from its point of origin. The energy industry relies heavily on data to maintain its standard operations, but energy data is rarely analysed in real-time or near real-time. Instead, it is normal to see up to hourly intervals for wholesale electricity pricing, and smart meters generally match this settlement infrastructure. There are then further delays before this data is transmitted back to the utility. This means that utilities are often reliant on data that is days old, making it challenging to efficiently balance supply and demand. A monthly, weekly or daily frequency is well-suited to most of the systems within the energy sector, but such frequencies are insufficient for active energy management. Monitoring data in real-time, on the other hand, enables consumers to better recognise and manage spikes in their consumption at certain times of the day. This means they can identify peak hours and make smarter decisions in order to optimise their consumption and reach their energy saving targets. Utilities can use this data to incentivise off-peak consumption. In peak shaving use cases, analysing consumption data in near real-time provides consumers with the opportunity to reduce their consumption during peak periods and positively influence their energy bills. 

Head meter data is the digital representation of a household’s total consumption and is therefore essential to any sort of household optimisation when it comes to energy.  The main issue, however, as mentioned above, is that the energy sector itself only requires smart meter data to settle electricity bills faster, meaning the industry does not require this data in real-time. When it comes to energy management, however, this data is only useful when it is very granular and provided in real-time or near real-time. re.alto recognises that there are many benefits to having access to smart meter readings in near real-time, especially when it comes to optimisation. The data can be applied to various use cases such as dynamic rates consumption, self-consumption, capacity tariff optimisation and peak-shaving. It enables the optimisation of a household in that the energy consumption in a home can be aligned with the energy production from the PV panels on the roof of the house, for example, to reduce the amount of excess energy injected back into the grid. To ensure that a household’s own installations consume most of the energy produced at home, it is necessary to know what is happening in near real-time. Or if you have a capacity tariff, this data can help you avoid peak consumption and reduce your bill by making you aware of exactly what you are consuming in real-time and in ensuring you do not increase consumption all at once, for example by cooking dinner and charging your EV at the same time. Spreading electricity consumption throughout the day can potentially reduce energy costs at a time when every saving helps. 

Great financial benefits lie in knowing in real-time exactly what your devices are doing and in possessing the ability to actively steer those devices. That is why we are developing the solutions to do just that. We obtain data via APIs for almost every energy-relevant asset, however, when it comes to head meters, the infrastructure does not currently exist, meaning the data has to be extracted in a different way. There are various methods of doing this in different countries in the European market, but the end game is ultimately the same: Obtaining data from smart meters in order to optimise energy management. That is why we are launching a new product focussed around capturing near real-time data from smart meters. Watch this space!  

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