Wednesday, April 20, 2011

Atmospheric Analytics

(republished from commodityweather)
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Google noted that a recent investment of $100 million will assist in enabling the Shepherds Flat Wind Farm to become the world’s largest wind farm by 2012; this follows their $168MM solar investment into the BrightSource Energy tower project.  The company has now invested nearly $350MM into clean energy to date.  Such investments from non-traditional cleantech investors are starting to receive more attention, and the sustainable increase of large scale infrastructure investments in the alternative energy sector will likely be accompanied by a rise in the demand for data-driven services that can help optimize efficiency of the related operational costs.

Enter the growing need for timely and accurate weather data.  Last month I touched upon the potential for the weather services sector to contribute their expertise to the smart grid arena.  Demand anticipation, efficient raw material utilization, baseload and peak usage forecasting, logistics planning  - these are just a few of the many areas where atmospheric analytics can contribute to this growing global market.  More frequent and more precise weather data can help utilities anticipate demand surges, and in the process reduce both unnecessary expenditures and unnecessary emissions.  Such supporting weather data is not just limited to the network of government maintained observation stations - cheap ubiquitous sensors can be placed just about anywhere, and more granular data that can help make a decision more efficient translates to more streamlined raw material procurement and utilization, not to mention lower costs passed on to the consumer. 

At many energy conferences of late containing the cleantech theme (look at tomorrow’s Green:Net event sponsored by GigaOm), there has been a lot of talk around the benefits of ‘smart’ meters and ‘smart’ algorithms which will in part be used to help transform the energy infrastructure.  These tools and techniques can only truly be considered ‘smart’ if they are embedded with ambient data feeds which can in turn supply timely and accurate data streams which can be developed into weather-driven efficiency algorithms (largely based upon persistence) - the resultant algorithms can then help to enable the energy management systems to operate in sync with their surroundings, in essence, becoming smarter.  As short range demand anticipation models are largely based on a set of standard assumptions, there will be limited human involvement once a system is constructed (as long as good input data is available), and this will fit in nicely with the functionality associated with automated demand response systems.  WeatherTrends360 provides examples of granular hourly weather data feeds and displays that can be embedded into such systems (see chart below).  While the cornerstone of applied modeling (garbage in = garbage out) is implicit, it should be noted that a smart algorithm will only be as good as the data upon which it has been trained. 





As the shift towards increasing the size of the share that renewables make up in the total energy generating matrix, the need for data services providing temperature, wind, solar analytics will strengthen.  Look for innovative ways in which weather industry, including both data providers and forecasters, can generate new sources of returns in this space, as the industry evolves.

1 comment:

Dennis said...

Good Article and I fully believe that the demand for higher resolution forecasts is going to grow quickly over the next 3-5 years. There is already a big push for more integration of "private sector" weather data to be incorporated into public models. Just look at the recent partnership between the DOE and NOAA.