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Location data is commonly used in the mobile and web industries as a way to provide context for services, and as an underlying platform for new applications. For example, cell phones and vehicle navigation systems use GPS location data to offer turn-by-turn driving directions, and websites like Foursquare have become popular for sharing manually-reported location data. In a similar way I think weather data could provide a platform for some very important next generation services and applications, particularly when it comes to energy efficiency.
We can thank communication technology — satellites, mapping databases, software and high-powered computing — for creating the ability to track and use real-time weather info. The National Weather Service, developed by the U.S. government’s National Oceanic and Atmospheric Administration, has been one of the key driving factors behind delivering this information in the U.S., and the organization provides “weather, hydrologic, and climate forecasts and warnings,” for “the protection of life and property and the enhancement of the national economy.”
Combine the National Weather Service data with Google and the open Internet ecosystem, and you have the makings of a platform for web 2.0 style services, not unlike how innovators are building applications off of mapping and location data. David Friedberg, the CEO of weather risk management company WeatherBill, which uses data from the NWS, explained to me in an interview last week that most of the private online weather data companies have built services on top of the National Weather Service’s XML feed and its API. There’s no doubt that the Internet has made real-time weather data more available, says Friedberg.
John Steinberg, CEO of smart thermostat software maker EcoFactor, which uses weather data to automatically adjust home owner’s connected thermostats, explained to me that in a pre-Internet world there would need to be one or more sensors on every home for EcoFactor get the type of weather data that it uses in its service. Steinberg says that EcoFactor works with a variety of weather data, but he didn’t want to disclose the sources for proprietary reasons.
Other infotech companies like IBM (s IBM) are building platforms leveraging NWS data and computing to develop prediction services specifically targeted at industries that rely heavily on weather data like travel, transportation, energy production, and agriculture. IBM (s IBM) sells a weather prediction service called Deep Thunder to municipalities, organizations and utilities, which use it to do things like tailor their services, change routes, or generate more or less power.
Weather data is moving down to the micro and personal level with better, more granular data, and the emergence of applications that can use weather data effectively. I Google my local weather on a daily basis, and objects like clock radios commonly that provide real-time weather forecasts. In a video on Deep Thunder, IBM research Lloyd Treinish explained it as: today we need super computers to deliver this type of prediction service, but someday this will be more widely available on a desktop and for individuals.
But as both Steinberg, Friedberg and IBM’s Director of Strategy for its Venture Capital Group Drew Clark all said to me, the really interesting part about weather data will be how it will be used automatically in systems. Whether it’s home and commercial building energy management systems that can automatically take weather data into account, or a large retail chain that could automatically starts stocking up on weather-related goods, weather data can make processes more streamlined, and importantly, making energy consumption more efficient.
Friedberg pointed out to me that there will likely be far less applications built upon weather, than say location and mapping data, because weather data is more used for direct consumption — it’s more like news (you ask what is the temperature or will it rain or not), and less like a real-time GPS location that has little meaning without its context. Weather forecasts are also less reliable — 75 percent chance of rain on Friday — so the applications would have to be less precise.
But I think in the context of energy generation and consumption in buildings, because heating and cooling and lighting are directly related to the weather outside of the home, there will be a wealth of applications that can be built on top of weather data and energy efficiency. EcoFactor is one of the first I’ve heard of that use this type of data directly and automatically linked to home energy consumption. It’s not coincidence that three-year-old EcoFactor was founded by Internet entrepreneurs (Steinberg, and SVP of Products Scott Hublou) looking to develop an energy efficiency product. In the course of a month EcoFactor says it can save 20-30 percent off of the heating and cooling costs on your bill and you won’t even notice.
Utilities already use weather data to try to predict and adjust to how much power generation they will need for the grid depending on micro local temperatures, storms, and wind factors. The difference in a forecast of a couple degrees equals a lot of investment — and a lot greater carbon emissions — in terms of unnecessary power generation for a utility. Georgia Power is using an application built off of Deep Thunder to adjust its energy generation swiftly to meet weather predictions. But ultimately weaving in automatic weather data into all aspects of energy generation and building consumption will require more robust utility networks, better weather forecasting data and smart systems that can better adjust to the real-time information. And that type of smart weather system could be a crucial part of the future of the smart grid.
Image courtesy of National Weather Service.