Where Do Data Scientists Fit in the Energy Industry?
- March 15, 2019
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The energy industry is so crucial that many people don't realize how much they depend on it unless things go wrong. Households are accustomed to flipping switches in their homes and seeing the room fill with light almost instantly. Due to the number of customers served and the extensive costs that could result due to unforeseen circumstances, many parties in the energy sector turn to data scientists for help.
Helping Power Plant Operators Conserve Energy
One of the emerging ways that the energy industry relies on data scientists is to learn how collected information could provide insights that reduce energy expenditures. Some companies in the sector still have outdated methods of recordkeeping and analysis, such as using spreadsheets. But, data scientists equip power plant managers to see what causes wasted energy and work to fix those issues.
Sometimes, data scientists use machine learning to predict possible issues before they happen. In that instance, a company manager could learn that certain pieces of equipment use more energy than necessary due to an improper maintenance schedule. Then, making a relatively minor tweak allows energy saving to happen in a way that might not have been evident without the help of a data scientist.
Predicting the Likelihood of Power Outages
High winds and other kinds of stormy weather often cause power outages that disrupt households and businesses. Researchers at Texas A&M University built the framework for a big data model that shows vulnerable electricity assets and indicates above-average possibilities that outages could happen.
Using that information, electricity providers can take preventive measures against outages, such as by trimming trees around power lines on a more frequent schedule than they did before digging into the data.
Giving Crucial Oil Field Insights
Problems with oil rigs and related equipment can be catastrophic for humans and the environment, often causing substantial long-term effects. However, data scientists can help energy companies stay continually aware of what happens in oil fields. Internet of Things (IoT) sensors deliver feedback about equipment in remote locations, letting data scientists recognize notable trends that could indicate issues.
Besides combatting the risks posed by issues such as oil spills, some energy companies use predictive analytics to forecast supply and demand. Taking that approach could help them stay competitive in an industry naturally prone to fluctuations.
Assisting Companies with Setting Up Data-Friendly Infrastructures
If energy companies have no digital infrastructures in place to manage and monitor their assets, they may hire data scientists to implement the required tools and teach on-site staff members how to use them. Other energy companies hire full-time data scientists and know that those employees are valuable parts of a team.
Current or aspiring data scientists who want to work in the energy sector may want to plan for an advanced degree. That's because statistics show approximately 88 percent of data scientists have master's degrees.
However, there are other ways to begin working as a data scientist in the field, especially for people with energy industry backgrounds who want to upskill. For example, enrolling in an energy industry boot camp is another option, mainly as people work towards their master's degrees but want to stay in the workforce as they do it.
Measuring Renewable Energy Specifics
More people around the world embrace the idea of renewable energy. A new report predicts wind and solar will make up 50 percent of the world's energy generation by 2050, while coal comprises only 11 percent. The study also goes into detail about how it'll become cheaper for energy companies to use solar equipment compared to fossil fuel options.
The rise of interest in renewable energy opens options for data scientists to work in the energy industry to track potential or actual renewable energy output and the factors that influence it, such as cloud patterns in satellite data. Then, if city authorities are thinking about putting solar panels on government buildings, for example, big data platforms and machine learning algorithms could assess the worthiness of such projects.
Also, in the consumer renewables market, some states with deregulated energy markets let customers sign up to entirely rely on renewable sources, then automatically switch them to different providers that offer that option depending on availability and prices. Machine learning can figure out the best plans for a household that wants to get fully on board with renewable energy without ongoing intervention from the customers.
Appealing Opportunities for Data Scientists to Shape the Energy Sector
This overview clarifies why data scientists are so instrumental to the present and future of the energy industry. Energy company leaders realize that data can help them reach definitive conclusions and stop relying on guesswork. As such, they can make confident decisions that save money and otherwise improve operations.