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Big Data Analytics: It’s Electric

Most Californians remember the California electricity crisis, which resulted in rolling brown outs and skyrocketing energy prices during 2000 and 2001. This energy shortage was caused by market manipulations, Enron’s illegal activities, capped retail electricity prices, delays in new power plants and drought. Energy supply could not meet demand, and for California utilities it was the perfect storm of cause and effect.

Today, Big Data and analytics combined with smart grid technology may make crises like what happened in California a thing of the past -- if utilities can resolve the Big Data analytics problem. Smart grid technology, including smart meters, promises to revolutionize energy by giving consumers more control; automating home energy consumption; reducing the cost to produce, deliver and consume electricity; enabling a larger penetration of renewable energy; identifying outages and resolving problems.

Smart grid technology also delivers unprecedented data availability. Today, utilities can collect meter data every 5-15 minutes for analysis purposes, as compared to five years ago where readings were monthly and only obtained for billing purposes.  All this data, coupled with sophisticated analytics solutions, will help utilities to evolve many aspects of their businesses, such as forecasting demand, shaping consumer usage patterns, optimizing unit commitment and asset allocation, and dynamically pricing units of energy.

Yet, we may be years away from realizing the full potential of smart grid technology. Two recent studies highlighted the adoption hurdles in deploying smart grids. According to a study from Oracle, only 17 percent of utilities feel prepared for the amount of data that smart grid is amassing, and 62 percent of utilities believe they have a Big Data analytics skills gap. Similarly, BRIDGE Energy Group reported that 55 percent of utilities are hampered by managing data through basic reporting and dashboards, and that they are not able to analyze data beyond description, classification and clustering. And only 10 percent of utilities are leveraging predictive analytics.

The good news with these studies is that the utilities realize where the gaps are. So, while we will continue to have “demand response events” – peak times when our local utility asks us to conserve energy – predictive analytics may soon pave the way to a brighter, more reliable energy future.

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