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Defining Quality Energy Data: What to Expect

In the world of energy data, quality often takes a backseat to quantity. Reliable and accurate data is essential for meeting customer demands, yet it has remained an expensive and elusive commodity.

Defining Quality Energy Data: What to Expect

The existing energy data on the market tend to fall short in terms of resolution, insight, and reliability. 

At Å Insite, we're committed to change that narrative by providing high-quality energy data. But what sets quality data apart, and why is so much of it unreliable?


Data on Component Level

To effectively manage power consumption, you must pinpoint the driving factors. Low-quality energy data merely provides aggregated figures for entire buildings or sections, which offers limited insight.

High-quality energy data, however, delves deeper, allowing real-time insights down to the component level. This level of granularity is where the data truly shines.


High-quality energy data give answers to key questions such as:

  • - Where and when is the energy consumption the highest?
  • - What constitutes the base load?
  • - What constitutes the high peaks?
  • - Which components are the primary energy drivers?
  • - Are some components using more energy than expecting?

When you have the answers to these questions, you are able to identify errors, habits and measures that have a significant impact on your energy consumption.


Correcting Errors

Most energy data is full of errors and to maintain a constant flow of correct data is resource demanding. High-quality energy data should have the ability to correct errors arising from installations, ensuring that processed data aligns with actual usage, even in cases of raw data gaps.

Problems with hardware, installation, and data transmission can lead to discrepancies, where 1 kWh no longer truly is 1 kWh. Detecting these errors and applying correction mechanisms are pivotal aspects of high-quality energy data.


Example From Real Life

A misconfigured power cable with phases in the order 2-3-1 instead of 1-2-3 led to incorrect meter readings.

A simple algorithm can rectify this issue, eliminating the need for costly reinstallation.


Tailored for the Right Purpose

Quality energy data goes beyond resolution and error correction; it must also be customized for its intended use. This means the data undergoes specific processing and is structured with metadata aligned with its purpose.

In essence, what you see as immediate information should cater to their primary measurement needs, in the most sensible resolution.

Some industries require minute-by-minute data on individual components for energy-saving measures, while in an office building, distinguishing between different areas ensures accurate tenant billing.

Å Insite offer high-resolution, reliable energy data without delays - for tenant invoicing, ESG-reporting, energy efficiency og optimization.


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