The importance of wind forecasting

Jeff Lerner, Michael Grundmeyer, Matt Garvert, 3TIER

With the recent and continued increase in wind power, the challenge is clear: how can the energy industry integrate this intermittent power source into the electricity grid? Jeff Lerner, Michael Grundmeyer and Matt Garvert of 3TIER make the case for better forecasting.

Traditionally, electricity utilities and system operators have sought to understand the supply side of load balancing with regard to the source of the energy, dispatchability and reserves, as well as the relative cost of producing electricity from non-renewable resources. With wind power increasing as a renewable energy source in many countries, the energy industry will need to adjust its thinking if it is to find an effective way to integrate this intermittent power source into the electricity grid.

And better forecasting will position wind for continued growth and penetration into the global energy mix.

The implications of the value of the forecast are:

  • Reduced imbalance charges and penalties;
  • Competitive knowledge advantage in real time and ‘day ahead’ energy market trading;
  • More efficient project construction, operations, and maintenance planning.

Accurate wind power forecasts are also important in reducing the occurrence or length of curtailments (which translate to cost savings), improved worker safety, and mitigating the physical impacts of extreme weather on wind power systems.


The suitability of a particular location for the development of a wind energy project depends on a large number of factors. Site characteristics such as accessibility during construction and the distance to transmission and load may dictate whether a site is suitable for development. Clearly, for wind energy development, the meteorological conditions at the site are of the utmost importance, since wind acts as the fuel in wind energy projects. Even though this fuel is free, no amount of money can buy additional fuel once a project is built. Project siting is therefore the single most important, controllable factor in determining whether a wind energy project will be economically viable or not.

Since direct observations of wind speed are only made at a limited number of sites, a comprehensive data set based on observations alone is impossible. Instead, computer models that simulate the dynamics of the atmosphere, called numerical weather prediction models or NWP, can provide important spatial and high temporal information on the wind resources at a site.

Proper assessment techniques using Numerical Prediction Modelling (NWP) modelling can provide valuable information on the expected diurnal and seasonal load for a project, as well as a long-term evaluation of the site's potential.

Understanding forecast error

A typical operational wind project will yield around 30% of the nameplate capacity over a year, based on the wind potential. This is a gross estimate which assumes all turbines are always online and running at optimal efficiency. In fact, several non-environmental factors can diminish a project's output potential, though many of which can be mitigated or efficiently dealt with.

Failing to forecast the wind power is a gamble that will prove costly over time. For example, the forecast error associated with no knowledge of the future wind power generation (an unskilled forecast) will likely exceed 40% of the plant capacity when verified against actual output. Figure 1 illustrates the magnitude of the forecast error as a function of the forecast lead time for a hypothetical 100 MW wind project. In this figure, three different forecasting approaches show the reduction in error using different methodologies for wind power forecasting.

Climatology represents the error associated with using a constant value forecast that is calculated from the average power produced for that project based on actual or reconstructed historical data. A persistence forecast is defined as taking the current power value and making that the constant power forecast for the rest of the forecast period (i.e. 50 hours in figure 1).

A more advanced forecast system such as the 3TIER model is based upon the output of both statistical and NWP models that integrate project power or meteorological observations.

Figure 1 shows that:

  • The persistence forecast is not that bad, on average, particularly for the short-range period;
  • 3TIER's advanced forecast system has the least error and improves upon persistence;
  • Forecast error, in general, increases with lead time, eventually approaching climatology.

How wind power forecasts impact the bottom line

We cannot overstate the financial implications of failing to use wind power forecasts. Imbalance charges resulting from deviations in scheduled output will be imposed on energy providers, increasing project operating costs. Fortunately, wind power forecasts can help to minimise these penalties, though they will not completely eliminate them.

Wind power forecasts can also reduce the significant opportunity costs of being too conservative in bidding output into a forward market, due to uncertainty of availability.

Prediction intervals

Based on the risk tolerance of the forecast user, prediction intervals can be incorporated into the decision making process for scheduling power production. The gray shading bounded by black lines in Figure 2 visually illustrates the concept of a prediction interval. The prediction interval encompasses the range of values in which the hourly-averaged observation falls in a given percentage of the time, in this case 80%.

The size of the prediction interval is determined by the historical forecast errors, which are a function of the forecast hour and the value of the forecast power and wind speed (source – Meade and Islam 1995). In Figure 2, forecast power values (thick orange line) display different prediction intervals in time: the 21-hour forecast at Tuesday 0300 prediction interval (see “(a)”) spans about 15% project capacity, whereas by forecast hour 84 (Thursday 1800, labeled “(b)”), the interval range exceeds 40% capacity.

A sharp prediction interval conveys more forecast certainty and could result in a less conservative view and lower opportunity costs concerning a energy bid. In general, as the wind power forecast becomes less certain for a longer term forecast, the width of the prediction interval increases. The key message of figure 2: it is important to accurately interpret the expected forecast errors and use this information in decision making processes such as energy scheduling and electricity trading, in which wind power can affect price fluctuations.

Saving on costs

What does obtaining forecast information mean in terms of cost savings to a trader, grid operator, or perhaps a wind tower maintenance scheduler? By employing advanced forecast information, the realised cost savings accumulate over periods of time when compared to using a non-skillful approach such as persistence or climatology.

Volatile electricity prices and load imbalances frequently result in wind energy curtailments in regions of the country in which the electricity transmission infrastructure has not been updated to manage the influx of energy from a variable energy source like wind. However, the frequency and length of curtailments are often due to under-utilisation of the information contained within a wind energy forecast. If grid operators know in advance when expected surges in cheap and clean wind energy production will occur, they may be able to reduce costs through the power-down of more expensive natural gas-fired plants.

Region-wide wind power forecasts are becoming increasingly available through forecast service providers such as 3TIER, thus removing much of the uncertainty associated with electricity bottlenecks. Another added benefit of regional wind power forecast is: lower overall forecast error, as individual wind project forecast errors tend to cancel each other.

Implications for maintenance

Forecasting can also be applied to save costs when operators need to schedule wind project maintenance and construction. Wind projects often require that turbines be taken down during the commissioning of new turbines. This can take hours to weeks depending in part on the weather.

For obvious reasons, precipitation, high winds and extreme temperatures need to be avoided. Without accurate forecasting information, the chances of idling a mobilised work crew and necessary equipment (such as large cranes) increases. Such associated costs can exceed US$10,000 per day. If the operator fails to take advantage of the right weather conditions for construction, operations, and maintenance, overall project costs can increase as deadlines are missed, plant generation is diminished, and resultant production revenues from Green Tags or Production Tax Credits are lost.

The forecast for wind power forecasting

So how accurate is wind power forecasting? Utility scale wind power forecasting is in its second decade and has seen great improvements with regards to quality, timeliness, and delivery of the forecast product. However, there are clear challenges facing the wind power industry and the science behind the forecasts (see tint box within the article).

It has been shown that forecasts of wind power are closer to actual wind power production when utilising a more advanced forecast product with state-of-the-art NWP models. Statistical methods such as Artificial Intelligence models incorporate project power and nearby observations for more accurate short-range forecasts than those obtained from persistence alone.

In the future, short-term wind power forecasts will benefit from longer historical forecast records and additional observations. Additionally, as NWP model ensemble forecasts are not yet being exploited for short-to long-range forecasting, better utilisation of NWP has enormous potential to reduce error.

In short, it is imperative that Government, academia and industry continue to work together towards more accurate weather forecasts that will benefit the renewable energy industry.

Current and future challenges to forecasting wind power

  • Making accurate very-short range (< 60 min) forecasts;
  • Improving ramp event predictions;
  • Incorporating climate change impacts on wind projects;
  • Integrating and automating regional forecasts with electricity scheduling systems;
  • Improving icing forecasts;
  • Improving probabilistic forecast product development using NWP ensembles.
About the author

Jeff Lerner, Michael Grundmeyer and Matt Garvert work for 3TIER, a global leader in wind energy assessment and forecasting.


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forecaster said

09 June 2009
As we get to finner resolution models, it is apparent that we also need more data in the initialization to remove errors. I beleive the key here to better forecasts, is to inrease the data input into the numerical models.

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