When you hear the phrase 'big data,' it's common to associate that with data about people. One of the most talked-about ways big data is used is targeted advertising, which uses many different types of data about people to target them with personalized ads.
There is data available from many sources other than people, however, and that data is beginning to make the energy industry more efficient regarding both power usage and cost. Here is a quick look at how big data can contribute to making energy cheaper and much more efficient.
IoT and Machine Learning
One way big data can help make us more energy efficient is by taking an Internet of Things (IoT) mentality and approach. Loosely defined, IoT is the process in which multiple everyday items are all connected to the web, which, in theory, means they can also communicate with each other.
So-called smart devices can also collect data over time. For example, a smart thermostat that's connected to the internet could track your temperature control over time. The system could then work together with other systems in your home to turn lighting, heating and cooling systems on or off either behind schedule or ahead of schedule to help make overall usage more efficient.
On a micro level, this may mean altering a home's temperature by just a half of a degree. Individual users probably won't feel the difference, but the energy it takes to produce that half of a degree adds up quickly when considering entire neighborhoods or cities.
Renewable Energy Integration
Another way big data can be used to make energy consumption more efficient is to optimize power grids by using more power from renewable sources such as wind and solar.
Using big-data analytics, wind and solar plants can track things such as the speed of the wind and the intensity of the sun at any given hour. That data can then be combined with weather-predicting information from satellites and radar to help determine near-future energy needs.
Then, when wind and/or solar power aren't enough, power grids can use their traditional backup sources such as natural gas. For individual homes or companies, that backup source may be a gas-powered generator, which itself can save 15% annually on costs if you have an efficient model.
It's not the data itself that makes energy consumption more efficient. It's the way data is used that makes all the difference. Knowing this, it's relatively straightforward to take data from the past to help predict what should happen in the future.
Taking oilfields as an example, machines and systems can 'learn' from the past and predict what should happen today and tomorrow. If those predictions don't match what's actually happening, the systems can trigger alerts to help operators quickly come up with solutions. This can be a big money-saver. Identifying and dealing with problems early is always better for production than noticing something when it's too late.
Continuing the oilfields example, machines can monitor individual wells in real time and alert people if production levels are subpar. In the past, it may have taken a day or more for humans to recognize such inefficiency. By using big data and predictive analytics, you can address these problems in mere minutes.
Every industry is learning how it can harness the power of big data to become more productive and efficient, and the energy industry is certainly no exception. As technology advances and such systems become more widespread, it only stands to make energy production and consumption cheaper and more efficient for all.