Big Data by itself isn’t actually that useful: It’s just an unintelligibly staggering number of zeros and ones.

But once advanced analytics are applied, Big Data transforms into an extraordinary asset for any industry, not the least of which are energy and utilities companies. These indispensable organizations can use advanced analytics for a variety of critical functions. Let’s look at what they are:

1. Energy exploration

Ever wonder how utility companies locate the resources that they deliver to customers? It’s called energy exploration, and in the 21st century, it relies heavily on Big Data and analytics. According to the Motley Fool, the most common means of identifying resource deposits under the Earth’s surface is through a method called seismic surveying. A powerful pulse is sent through the ground in order to create a virtual, subterranean map. In other words, it makes data.

Over the course of years, thousands or even hundreds of these maps might be created, each containing countless amounts of data that, when analyzed, may house clues about where else to look. When the time for actual extraction comes, Eniday contributor Peter Ward noted that analytics are capable of “gathering pressure, volume and temperature data and comparing it to historical databases in order to look for patterns of equipment failure.” The result is an easier time finding natural resources and subsequently extracting them for consumption.

Electricity companies are already using Big Data and analytics for smart grids.Electricity companies are already using Big Data and analytics for smart grids.

2. Smart metering

One of the more directly consumer-facing ways in which utilities use Big Data and analytics is smart metering. In a nutshell, smart meters are devices installed in commercial or residential properties that quantify the amount of electrical energy being used. This information is then sent back to utility provider, typically multiple times per day. The immediate goal is to get an accurate reading of how much energy is used during certain hours, but there’s a longer-term objective at play here according to ComputerWeekly contributor Lindsay Clark.

“The ultimate goal of smart metering is to allow utility firms to forecast energy usage, to improve their performance on the settlement markets – where money can be lost through inaccurate predictions – and to match supply and demand more closely,” Clark wrote.

To achieve this, utilities will not only need to aggregate and store massive amounts of data, but they’ll also need a powerful analytics engine with predictive capabilities. Moreover, because this information is coming from disparate sources (scattered residential homes and businesses), much of it will live in the cloud, which means that cloud analytics will be an essential component of deriving actionable insight from smart meters.

3. Faster maintenance and troubleshooting

The operational expenses for any utility provider – water, oil, gas, electricity, etc. – are significant. Any opportunity to scale back on OPEX is welcome, and that’s exactly why industry leaders have begun to explore how Big Data and analytics can be used to detect equipment failures and maintenance issues before they actually happen.

“Fix problems before customer service requests are issued.”

These predictions, which are arrived at using machine learning technology that parses through thousands of historical data sets, can be used to identify the earliest indicators that something that might be wrong. In doing so, they can help prevent oil leaks that have the potential to devastate the environment, as well as water leaks in homes, which is hugely important in parts of the country that are suffering water shortages at present.

Furthermore, the ability to fix problems before customer service requests are issued is also hugely beneficial. It saves contact center workers the heartache of having to listen to anguished customers, it saves customers the anguish, and it saves time and money for the utility provider that can be focused elsewhere, such as looking into alternative energy.

4. Identifying viable alternative energy sources

Many utilities have begun looking into new energy sources that aren’t in the traditional scope of those services they offer. Doing this requires a significant amount of research and data aggregation to determine what alternative sources are available, how they can be offered to clients and where they will be most effective.

The most recent, well-known example of an effort on this front came from a company that makes its many billions of dollars from its ability to aggregate and analyze Big Data: Google. In 2015, the internet giant released Project Sunroof, which is a Big Data tool that can tell users how much money they will save over the course of two decades by installing solar panels at a given address.

The program draws data from myriad sources including Google Maps – which identifies tree coverage, clouds and more using deep learning – weather data from the National Renewable Energy Laboratory (NREL), seasonal conditions and even the positioning of the roof. With this data, Project Sunroof can estimate how much energy solar panels can generate at a given location. It then calculates the cost of solar minus the tax incentives, and supplies an approximation for the amount of money saved by switching to solar.

Initiatives such as this can not only be used to evaluate the efficacy of alternative energy sources, but they can also be leveraged by innovative utility companies that want to get a running start in what may be one of the leading energy sources in the future.

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