Lake Street's aim is to help companies 'work with the weather'. In other words, we look to deliver high value meteorological services that enable the end user to take informed action. Invariably this means processing weather data alongside datasets from the end user’s sector.
Using an example from the growing renewable energy sector, we will show how private sector companies are able to identify the relevant datasets – weather and sector specific, translate weather variables to power generation, and present a product that enables informed decision-making.
Having accurate weather information enables efficient dispatch of fossil-fuel generators when required to match demand, and so helps nations work towards net zero. This includes information about temporal and spatial correlations that are not immediately obvious from raw weather model output.
Different models are best for varying timeframes (think within day compared to next week) making no single weather model sufficient on its own. Further, the timetable of the network operator determines the cut off time for model information, and so influences model choice.
Weather forecasts are not exact and model errors can be considerable. Knowledge of uncertainties aids decision-making, yet this information is often one of the greatest challenges to calculate.
Private sector companies act as the translator, and add value at the last step, but could not do this work alone. They draw on the work of national and international bodies and academia, and in turn feedback observations about forecast error enabling model improvement. With collaboration, we increase the value of meteorological services and their usefulness to society.
How to cite: Finney, I.: Forecasting renewable energy generation: an example of how the private sector adds value, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-242, https://doi.org/10.5194/ems2022-242, 2022.