The Growing Impact of Data Science in the Energy Industry

Challenges and Transitions in the Energy Sector  

The energy sector is facing a number of challenges and transitions that are drawing increasing attention to the need for modernization and greater infrastructure resilience via new technologies and big data analytics. 

• Increases in energy demand, energy generation and storage decentralization, the decarbonization of energy supplies, energy democratization, and increases in energy demand are among the most important industry transformations.
• Fossil fuels comprise a large percentage of electricity generation across the globe, and contribute to 61 percent of electricity generation in the U.S. 
• Growing electrification throughout every sector within the U.S. economy is predicted to continuously escalate electricity consumption by upwards of 38 percent by 2050, according to the U.S. Department of Energy.
• The power distribution infrastructure in the U.S. is rapidly aging and increasingly stressed with outdated and unreliable equipment. This is resulting in the inability to optimize capacity to meet demand, congested transmission lines, drastic price fluctuations, pervasive power failures, and even wildfires. 

Also, though the energy industry is a critical enabler of anthropogenic climate change, the industry has a distinct shortage of engineers with the requisite data science skills to help organizations in this sector utilize massive quantities of collected data to drive holistic transformation. This is due to several factors, including the overall global gap between data science talent and demand, and a frequent lack of emphasis in graduate-level energy engineering programs on data literacy and programming skills in favor of technology and policy. 

Emerging Data Science Use Cases in Energy and Utilities

Organizations in the energy industry are increasingly investing in initiatives and solutions that leverage data science to provide new means of optimizing energy usage, offer energy generation alternatives, reduce waste, and streamline operations.

Operations Optimization 

Digital twins can help energy organizations leverage resources more efficiently and productively by planning outages, minimize operational risks, and mitigate unplanned downtime, events, and lost production. Energy organizations have improved reliability by 99 percent in under two years and decreased reactive maintenance by 40 percent simply by using digital twins.

Detect Defects

AI plays an important role in identifying anomalies across the energy production line, including those in machines or wiring, and any defects in processes that are vulnerable to faults. Predictive analytics can alert operators to any changes to equipment stability to improve safety, and deep learning models can potentially automate energy grid processes to alleviate grid congestion.

Smart Grids

Data analytics can be leveraged in grid modernization efforts to introduce resilience into the energy ecosystem and address vulnerabilities in the current grid system. For example, energy can be allotted to where it’s most needed based on demand, rather than providing a constant energy flow. This also enables renewable energy to be better utilized when it’s most available, and helps mitigate power outages. Furthermore, smart grids can help energy companies anticipate and mitigate potential risk incidents — such as wildfires — by leveraging sensors, data, and analytics while also enabling critical resources like vegetation management teams to be efficiently deployed. 

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Rethinking an Energy-Efficient Future

With data scientists spearheading transformation initiatives, the energy sector offers an abundance of opportunities to positively impact climate change and sustainability efforts, and reduce dependency on fossil fuels. Data science will continue to be vital to enabling the energy industry to respond to quickly evolving needs and better prepare for a more energy efficient future.

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