Across the globe, farmers, air traffic controllers, financial planners and emergency agencies are just a few of the types of people that could seriously benefit from better forecasting.
For Swedish energy company Tekniska verken, imperfect weather forecasts are especially damaging, as they have to predict exactly how much electricity needs to be produced from their wind turbines and put into the energy grid 24 hours in advance. This prediction is what the system operator relies on when balancing the energy grid. When electricity grids aren’t properly balanced, this creates major costs for energy companies, often adding up to around 7 percent of the total profit.
“If the prediction is either too low or too high, we will suffer the economic consequences”, says Erik Olsson, business developer at Tekniska verken.
Unlike gas, electricity can’t be stored in large quantities, and thus the ordered demand and supply must balance every day. With wind energy, both the demand and the supply – the wind itself – can be better predicted with stronger weather forecasting, thanks to AI.
This predicament extends into electricity trading as well. Olsson explains, “If you know exactly how much the wind turbines will produce for, let’s say, the next 48 hours, you can sell it on the market for a higher price. This makes extremely detailed forecasts so valuable for us.”
Tekniska verken is using Peltarion’s cloud-based AI platform to develop a deep learning model to improve weather forecasting. They’re calling it Deep Weather as it identifies patterns in both live and historical weather data using cutting-edge machine learning algorithms. Deep Weather’s ability to process larger amounts of data with better and faster computing can help Tekniska verken steer into a new era of weather forecasting.