Statistics for Big Data For Dummies
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One area where big data has made an impact on electric utilities is the development of smart meters. Smart meters provide a more accurate measure of energy usage by giving far more frequent readings than traditional meters. A smart meter may give several readings a day, not just once a month or once a quarter.

The information gathered by these meters help customers conserve electricity (by providing them with a more accurate picture of their consumption patterns). It can also enable them to better plan their electricity usage to avoid peak hours and save money.

Smart meters also provide utilities with several advantages:

  • More accurate forecasts of future energy demand

  • Improvement in the scheduling of maintenance

  • Increase in ability to detect fraud

  • Reduction in power outages

  • Better compliance with regulatory requirements

With smart meters, utilities can determine not only how much electricity is being used, but at what times of day it's being used. This information is critical in forecasting the demand for electricity at peak hours. Because electricity can't be stored, capacity must match use at peak hours — the rest of the time, much of this capacity remains idle. As a result, the more accurately utilities can measure peak demand, the more efficiently they can plan for capacity needs.

The biggest challenge to the utilities that use smart meters is that the amount of data being generated is dramatically greater than the amount generated by traditional meters. This fact requires a massive upgrade in the hardware and software capabilities of many utilities. Another problem is that the data being gathered may come from many different sources, leading to potential compatibility problems.

In the long run, the investments being made by utilities in big data capabilities may end up saving money by using existing resources more efficiently, thereby reducing the need to build new capacity.

About This Article

This article is from the book:

About the book authors:

Alan Anderson, PhD, is a professor of economics and finance at Fordham University and New York University. He's a veteran economist, risk manager, and fixed income analyst.

David Semmelroth is an experienced data analyst, trainer, and statistics instructor who consults on customer databases and database marketing.

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