WHEN Mitsubishi Power Systems Europe, a subsidiary of a Japanese industrial conglomerate, announced on February 25th that it was investing £100m ($145m) to establish a new wind-turbine research and development facility in Britain, the deal was heralded as another step forward for wind power. But the timing was in some ways unfortunate. Although Britain is indeed a good place to put wind turbines, the wind, like the weather, is notoriously variable. And according to data from the Met Office, Britain's national weather service, February 2010 was in fact the least windy month for seven and a half years.

Wind power is widely seen as the source of renewable energy with the best chance of competing with fossil-fuel power stations in the near term. The European Union has committed itself to getting 20% of its electricity supply from renewable energy sources, mainly wind power, by 2020. In America the Department of Energy reckons that wind could provide a similar proportion of the country's electricity by 2030. China recently tripled its wind-capacity target to 100GW by 2020.

But capacity does not equal electricity. For all those turbines to be worthwhile, the wind has to blow in specific places at specific strengths for specific periods of time. What if it doesn't? Such questions are at the root of a growing interest in the field of wind forecasting. It can help developers of new wind farms decide where to build, and help operators of existing ones forecast output more accurately.

Wind power may be free, goes an industry adage, but it is far from cheap. Global investment in wind farms worldwide was $14.1 billion in the first quarter of 2010, according to Bloomberg New Energy Finance, a market-research firm. To secure financing for their capital-intensive projects, developers need to convince sceptical lenders that they will deliver the promised returns. This requires credible estimates of the future wind capacity of a given site. Unlike coal-fired power plants or nuclear reactors, each wind farm's electricity-generation potential is determined largely by its local physical environment.

The main tool for assessing the wind resources at prospective wind-farm sites is “measure, correlate, predict” (MCP) analysis. This involves comparing a short-term sample wind measurement at the site itself with years' worth of historical wind data taken from a nearby airport or permanent weather station. Using statistical models to take account of the difference between the target site and the reference site, developers can then build a detailed picture of the potential wind resources.

Don't bet the farm

Uncertainty lurks in all three aspects of MCP analysis. Technical problems with anemometers can make it difficult to establish a reliable record of wind patterns. Given the sensitivity of wind to the shape of the landscape, there may be little correlation between the target site and the reference site. And wind turbines are often much taller than measurement towers, making it necessary to take account of the variation of the strength of the wind with altitude, which is known as “wind shear”.

This can be particularly difficult in the vicinity of complex terrain or forests. A study by America's National Renewable Energy Laboratory in Indiana in 2009 found that a 50-metre difference in elevation resulted in a variation in average wind speed of more than 4kph (2.5mph). Since power output is proportionate to the cube of the wind speed, even such an apparently small variation can result in a 15% change in a wind farm's generating capacity.

Producing an MCP forecast with a small error margin—in other words, a high degree of certainty—is critical to establishing a site's commercial viability, says Tom Murley at HgCapital, a private-equity firm with investments in renewable energy. Before stumping up a loan for a wind project, banks need to be convinced that a site has sufficient wind resources. Rather than basing this estimate on the average of the observed wind measurements, banks lend on so-called “conservative wind”.

To calculate this, developers use a statistical model to obtain a “P90” wind value—the average wind speed in which they can be 90% confident. The closer the P90 reading is to the measured average speed, the more attractive the site becomes to investors. If the P90 wind-speed is within 12-15% of the average, banks are usually happy to stump up. But a difference of 20% or higher renders a wind farm “unfinanceable”, says Mr Murley. Conversely, reducing the error margin to 7-10% can reduce a project's cost of funds by 0.5-0.75 percentage points, resulting in higher investor returns.

To establish a more accurate picture of wind speed and direction at prospective sites, some companies are turning to laser and sonar-based measurement instruments to complement MCP. SgurrEnergy, a Scottish engineering firm, for example, has developed the Galion Lidar, a device that measures wind speed, direction and shear by measuring the distortion of pulsed laser beams as they are buffeted by microscopic wind-borne particles. The company says the use of lidar can reduce the error margin by several percentage points, which in turn increases a project's internal rate of return by around one percentage point.

Once wind turbines are up and running, the forecasting focus shifts from long-term averages to short-term specifics. For grid operators trying to balance a portfolio of different power sources, intermittency is no longer an abstract statistical anomaly, but determines whether they can keep the lights on. In Denmark, which already gets nearly 20% of its electricity from wind power, a change in wind speed of one metre per second can translate into a change of 450MW in national power output, equivalent to the entire capacity of a coal-fired power station, says Poul Mortensen of Energinet.dk, the main grid operator.

For very short-term predictions (up to an hour ahead), the dominant technique is “persistence forecasting”, which works on the simple assumption that wind speed in an hour's time will be the same as it is at the moment. Despite the huge amount of investment that has been put into wind forecasting in the past decade, persistence “is still the benchmark” for short-term forecasting, says James Cox at Pöyry, an energy consultancy. But this approach is less accurate when looking further ahead.

To improve upon persistence forecasting, companies rely on numerical weather prediction (NWP), the approach used by meteorologists to produce national weather forecasts. This involves modelling the atmosphere as a three-dimensional grid, each cell of which is a few kilometres on each side, and combining physical data on initial conditions such as pressure, temperature and humidity, collected from sensors and satellites, with equations that simulate the behaviour of the atmosphere. The resulting estimate for wind speed in a particular location can then be converted into an estimate of electricity generation using a power curve, based on the capabilities of specific turbines.

A big challenge for NWP in wind prediction is that local variations in topography or terrain can produce large variations in wind resources over relatively small areas. One way to address this is to increase the resolution (ie, reduce the cell size) of the grids that NWP models use to map the prediction area. In some cases the resolution of the computational grid is increased around areas of interest, such as mountains, coastlines or individual weather features. This approach is taken by AWS Truepower, a consultancy, with its eWind forecasting service. The firm says eWind can reduce the hour-ahead prediction error from as high as 7.5% for persistence forecasting to as low as 4% of a wind farm's rated capacity. For next-day forecasts the prediction error is reduced from over 35% for traditional NWP models to 14-22%. Similar results have been achieved in Denmark, where the next-day forecast error has been reduced from 36% to 18-20% over the past ten years, says Mr Mortensen.

A collaborative project between Xcel energy, a large American utility, and America's National Centre for Atmospheric Research (NCAR), based on data from Xcel's 2,500 wind turbines in the United States, uses a “nested grid” configuration based on overlapping 30km, 10km, and 3.3km grids. According to David Johnson of NCAR, the higher-resolution models enable his researchers to observe the effects of different terrain such as gullies or valleys within a single wind farm and to make an individual prediction for each turbine.

This high-resolution model shows encouraging results, especially when coupled with real-time observations from the wind farms themselves. With the aid of ever more powerful computers, the increase in model resolution will “continue to reap rewards” for wind forecasting, says Daniel Kirk-Davidoff, a meteorologist at the University of Maryland.

What's it worth?

Another way to take account of the unique characteristics of individual wind farms is through the use of artificial neural networks, which compare forecast results from NWP with actual turbine data from the wind-farm operators in order to “learn” the biases of a specific site and make more accurate predictions in future. But persuading wind-farm operators to part with the data—which many regard as commercially sensitive—is often easier said than done. Bruce Bailey of AWS says the reluctance of operators to part with turbine data is “the biggest barrier to improving the state of the art of forecasting”.

For independent wind-farm operators selling power into the grid, accurate short-term prediction can mean the difference between large profits and even larger fines for non-compliance. More accurate forecasts also benefit grid operators and their customers, because less fossil-fuel-based reserve capacity needs to be kept on hand in case the wind drops. Xcel expects to save $10m-12m in the first two years of its collaboration with NCAR, which should eventually show up in lower utility bills. The biggest challenge facing wind farms is to “look as much as possible like power stations”, says Andrew Garrad of GL Garrad Hassan, a consultancy. More reliable forecasts of the vagaries of their free but fickle fuel supply will help them do so.