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According to a recent study, wind forecasts generated by NOAA weather model is energy saving- around $150 million in energy savings per year. The wind is abundant as well as intermittent. Thus, utilities require precise wind forecasts to calculate electricity production and to estimate when they need to purchase or generate energy from other sources when wind speeds are not steady and continuous.
Older and inaccurate forecasts cost large sums of money, and those costs are then passed on to consumers. Accurate and advanced forecasts result in more systematic generation management and considerable savings.
The study appeared in the Journal of Renewable and Sustainable Energy and was conducted by scientists and economists from Colorado State University and the NOAA’s Global Systems Laboratory. The study examined the impact of NOAA’s HRRR (High Resolution Rapid Refresh) model. The research team found out that curiously accurate weather forecasts over the last decades have netted consumers over $150 million every year in energy savings.
Dave Turner, manager of the Global Systems Laboratory’s Atmospheric Science for Renewable Energy Program, stated that the study examined the costs to the utilities of both overprediction and underprediction. Overprediction needed the utilities to purchase energy off the spot market, while underprediction resulted in costs associated with the unneeded burning of fossil fuels.
Researchers compared the outputs generated by older and newer versions of NOAA’s HRRR weather model to evaluate the economic benefits of much more precise wind forecasts. The HRRR model generates predictions of wind speed and direction data at different levels of the atmosphere. Utilities can then measure how much energy their turbines will produce.
At intervals every few years, the NOAA researchers transition an updated version of the HRRR model to National Weather Service operations. Researchers are currently planning to evaluate another benefit the HRRR offers to the renewable energy industry- improved cloud cover forecast for solar power providers.