"Big data" is a big deal for electric utilities.  In a smart power grid, when you turn on the lights, you're generating data. When your neighbor plugs in his electric vehicle, he's generating data. When a heat wave causes retailers to crank up the air conditioning, they're generating data.

All of that usage data adds up to what we call big data. And usage data is just the demand side of the energy data spectrum. Utilities also rely on huge amounts of data to trade energy on the open market, manage power plants, optimize renewable integration and make decisions regarding risk, customer engagement and workforce planning.

But utilities also ask: What is the business value of our big data?  It costs money to capture and store and share these bits and bytes, so what are we going to get out of it? 

The value is only derived when transforming the data into information. And that’s exactly the opportunity for electric utilities today – harnessing new volumes of data to radically improve load forecasting, which means they make more prudent decisions about power generation and energy trading.  These improvements, which can significantly improve financial return, are gained by applying sophisticated forecasting and predictive models to ever-more granular data via a new analytical solution, SAS Energy Forecasting, announced this week at The Premier Business Leadership Series.

Utilities in all geographies must generate or purchase energy to satisfy expected customer demand.  This forecast, largely influenced by weather predictions, is becoming more complex due to increasing renewable energy sources, rising volatility in demand, and floods of new data from smart meters. Getting the forecast right not only helps to keep the lights on, but it keeps costs down for businesses and consumers.

For more information about analytics and the smart grid, I invite you to download one or both of these research papers:

• The Soft Grid 2013 - 2020 – A review of the opportunities and challenges in the Utility IT landscape, by GTM Research.
• When One Size No Longer Fits All - Electric Load Forecasting with a Geographic Hierarchy – A look at the best practices for a hierarchical forecasting methodology.