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How EnergyMetric converts data to intelligence

In this article, Professor David Brayshaw explains the processes Energy Metric use to convert historic climate data into climate intelligence.
© University of Reading

In the previous Step, Maria introduced the work of EnergyMetric – a climate service tool which has been developed to aid long-term planning in the energy sector.

Maria described how the service supports stakeholders who are making radical decisions about the transition towards low-carbon or ‘net-zero’ energy systems. This energy system transformation is profound and is taking place rapidly, with the next 10-20 years as a core planning timescale. The challenges included:

• Extreme weather hazards (eg, flooding and wildfires)

• Increasing levels of weather-sensitive energy demand (eg, use of electricity to support increased irrigation)

• Increasing use of weather-sensitive energy generation (eg, wind and solar power).

Stakeholders therefore need actionable climate intelligence on the potential for weather hazards that may cause damage to infrastructure and assets, but also characterisation of weather-driven demand patterns and renewable energy generation.

Illustrative example from EnergyMetric of graph indicating hourly demand energy daily ramp rate for 2009. The data provided for time of year (y axis) and time of day (x axis)

Illustrative example of an EnergyMetric visualisation tool to improve transparency. Heatmaps covering generation, demand, residuals, and ramp rates allow users to explore high and low production across multiple years. Click to expand.

Using historic climate data

Maria’s discussion highlights how EnergyMetric ‘solves’ the characterisation of renewable energy generation using publicly available climate data from the Copernicus Climate Data Store. They base their solution on 10 years of historic climate data, using this data to create maps and time-series of, for example, renewable resources (which can be explored through a user-friendly interface).

At first glance, this reliance on historic data may seem strange for a service setting out to support decisions relating to climate in the future. However, Maria also notes the following about EnergyMetric users:

  • Having detailed spatial information is particularly important – users wish to know the weather and climate at particular sites.
  • Looking at the relatively near-term planning timescales involved (just 10-20 years ahead) due to the extent of the change taking place in the energy system the impact of long-term climate change is relatively modest.
  • Require detailed quantitative information at the end of the process.

EnergyMetric therefore opted to rely on a sample historic climate. Starting from a publicly available ‘coarse grain’ (approximately 30 km grid boxes) climate data sample, they ‘downscale’ this to a much finer grain with a high quality but computationally expensive climate model, before ‘converting’ the raw meteorological information (eg, wind speeds) into energy information (eg, wind power).

Illustrative example from EnergyMetric - graph shows how much energy does a scenario generate annually from renewable sources

Illustrative example from EnergyMetric which allows users to quantify and understand uncertainty. Results by portfolio, generation type or installation. Click to expand.

The stages of this process are explained in more detail in Week 2. The basis lies in combining a so-called meteorological ‘reanalysis’ (Step 2.2) with a numerical climate model (for the downscaling, (Step 2.5), and a quantitative ‘impact model’ (Step 2.9). Many assumptions and choices are being made in this process, involving careful judgments about their appropriateness and validity.

Move on to the next Step to consolidate your understanding of this case study.

© University of Reading
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Climate Intelligence: Using Climate Data to Improve Business Decision-Making

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