Powerful
forecasting
methodology

Our unique power generation models map fundamental weather variables to the total power generated by renewable technology.

Wind power model

Historical wind speed total generated power

Wind power generation models are created by evaluating correlations between the historical wind speed and the total power generated. A proprietary algorithm is used to forecast an effective wind speed that is appropriate for the whole country, which is then used to forecast generation.

Available for the following countries:

Italy, France, Spain, Czech Republic, Denmark, Netherlands, Poland, Romania, Turkey, Sweden, Portugal

Solar power model

Historical solar irradiance total generated power

Solar power generation models are created by evaluating correlations between the historical solar irradiance and the total power generated. A proprietary algorithm is used to forecast an effective solar irradiance that is appropriate for the whole country, which is then used to forecast generation.

Available for the following countries:

Italy, France, Spain, Czech Republic, Belgium

wind power icon solar power icon

Accuracy

Measured using appropriate methods, such as the
mean absolute percentage error

 
 

6%

Error

10secs

To deliver our forecasts

A process you can trust

  • 1

    Identify generator locations

    We regularly purchase detailed generator location databases, and these are manually checked and verified.

  • 2

    Collect and
    pre-process data

    We download accurate generation figures from the appropriate sources, which is done automatically and in real time to ensure only the most up-to-date information is included in the forecasts. The TSO data is then processed with a range of statistical methods to eliminate outliers and further improve the quality of the data set.

  • 3

    Obtain weather data

    We work directly with weather companies to process, format and store the relevant weather data.

  • 4

    Aggregate data

    In order for our models to work, we have to reduce the weather variables to a single value.

    For wind, we:

    • optimise the generator set on a forecast-by-forecast basis
    • perform a capacity weighted average for all the generators in the optimal subset

    This results in a wind speed that is representative of the country.

    For solar, we:

    • follow the same process, except include a proportion of a country’s rooftop solar within the forecast
    • back calculate the data to best fit the generation data
    • perform a capacity weighted average for solar farms and rooftop generators

    This results in a solar irradiance that is representative of the country.

  • 5

    Apply power generation model

    We select the best model based on a variety of error metrics, using a similar approach for the majority of forecasts.

    For wind, we use:

    • a regression-based statistical fitting on a training data set
    • an optimal amount of recent data to forecast on a case-by-case basis
    • the aggregated wind speed and our fitting data to calculate the power forecast

    For solar, we use:

    • a daily generation profile, fitted through cumulative solar irradiance data, and a training data set
    • a training data set pulled from the repository of actual generation
    • an optimal amount of recent data to forecast on a case-by-case basis
    • the aggregated solar irradiance and our fitting data to calculate the power forecast

    In operational forecasts, wind forecasts are sent in 1, 3 or 6 hour packets, and solar forecasts are sent in 24 hour packets. These packets are sent within 10 seconds of the data being received.

    A similar process applies to ensemble forecasts, except the packets are sent within 20 seconds of the data being received. This is because we run between 30-50 different forecasts to calculate the mean, minimum and maximum values.

  • 6

    Automate delivery

    We have our own supercomputing cluster, as well as a full redundancy system as backup, which currently operates at >99% availability. All files are delivered as soon as they are ready via SFTP – typically within seconds.

  • 7

    Assure quality

    We benchmark our forecasts against TSO and other relevant sources. Our models are continually evaluated for quality and speed, so if a model goes off track, corrective action can be taken immediately.

  • 8

    Think to the future

    We are always researching new ways to improve our offering, including advanced data filtering, better outlier detection, ensemble calibration, statistical fitting, machine learning, and analogue forecasting.

8 reasons we'll work well together

1

Processes are accurate and continually improved

2


Data quality is checked

3


Weather data is obtained

4


Our R&D investments secure the next generation of forecasts

5

Data collection is streamlined

6


Your team members have more time to focus on the core business

7


Your budget allocations are optimised

8


Forecasts are in safe hands

Additional services

As well as delivering the most impactful wind and solar generation forecasts, we offer the following services:

  • Complete ensemble statistics
  • Live hourly updates
  • Demand modelling
  • Single site forecasting

See for yourself

For bespoke country reports, information about our additional services, or to keep up-to-date on all our latest news, please get in touch and find out what our power forecasts can do for your performance.

Give us a call

+44 (0) 117 214 0348

Email us

[email protected]

Leave us a message

+44 (0) 117 214 0348

[email protected]

6th Floor, Newminster House, Bristol, BS1 1LT, UK