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Good and bad weather stations

Hugo Birkelund
Archived blog post. This blog post has been transferred from our previous blogging platform. Links and images may not work as intended.

EQ research shows: Removing “bad” weather stations substantially improves the quality and stability of power consumption forecast systems.

In general, you need to find the correct weather stations if you want to succeed in modelling the weather-driven parts of the power market. Using a sophisticated model, but skimping on included stations, could even make matters worse. Even if you want to report something as simple as the average temperature in an area, this is a golden rule. If the weather stations are ill-chosen, the spatial-averaged temperature will be far off whatever you want it to represent. And the inherent error will be passed on to subsequent models that use temperature, or any weather parameters, as input. Be it consumption, hydrology, hydro production, wind power, solar photo voltaic power, or any of the models mentioned in the list beneath.

But how to choose the correct stations? And why not use all available stations? Some findings from EQ researches on our new consumption system may shed some light on this.

Consumption and needed input

As a rule of thumb, modelling power consumption is all about capturing reoccurring patterns. A bit oversimplified, this modelling task may be parted in capturing two effects: One part of consumption is determined by the social patterns. The other is dependent on how humans adapt their power demand to the climate in general, and current weather in particular.  

Social pattern: Humans' adaptation in terms of power consumption to e.g. time of the day, work pattern over the week, vacations and moving holidays.

Weather/climate: Created by earth orbiting the sun, and how individuals have adapted to local weather conditions for e.g. temperature, overcast/daylight, humidity, wind-chill, heat-radiation, etc.

Capturing the climate and weather-induced part of the power demand, rely on being able to forecast the weather and affecting factors with sufficiently high spatial representation and accuracy. And this is where weather stations come into the question.

The spatial part is solved by adding what we term a sufficient number of weather stations. However, each weather station added incurs costs in terms of time and money spent on maintenance. So conventionally the business standard has been to limit the number of stations included. One might call it the strategy of good-enough.

The EQ approach

EQ acknowledges that the time for this strategy may belong to yesterday. Increased request for higher accuracy, more focus on understanding and forecasting local conditions, and the requirement for higher time resolution, all points to this.

So, setting up our new consumption model system EQ adopted the strategy of using all available weather stations that fulfill quality criteria. In effect, we have removed what we have termed “bad” weather stations. “Bad” in this context is one station, either lacking or showing a poor update of actuals, or simply one located far away from where power consumption is decided.  

Following this modelling strategy has:

  • Substantially improved the quality and stability of the new EQ power consumption system
  • Shortened the lag time for actual data, meaning time from now and until last updated data from a station shows on our web
  • Led to a massive increase in included stations, compared to what we believe is conventional for the business
  • Still, about 30% of all available stations in Europe were removed that critically rely on choosing correct weather stations

Tags: Temperatures, Wind speed, Wind direction, Precipitation, Moist, Evaporation, Air pressure, Cloud coverage, Solar radiation (array of types), Snow. Power demand, Wind power production, Solar photo voltaic production, Precipitating energy, Wind-Chill index, Heating index, Cooling index, Overcast index, Cloud covers, Run of river production, Reservoir production, River temperatures.

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