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Examples of Data Science in the Energy Industry

Data Science > Examples > Energy

The energy and natural resources sector is more diverse than ever before. Prompted by government initiatives, the world is increasingly leveraging renewable energy sources like wind, solar, minerals, metals, hydrocarbons, and more to join oil and gas as major contenders in the global energy landscape. 

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The vast data streams associated with these various energy sources present incredibly valuable opportunities for energy professionals to gain insight into an array of industry factors, such as production, reserves, economics, inventory, commercial workflows, finance, and more. It is only with the use of data science in the energy industry that we can fully harness the power of energy data and make better, data-driven decisions. 

Data science for energy facilitates the processing of vast amounts of structured, unstructured, historic, and real-time data, which is exploding in volume, variety, and velocity. This data can then be thoroughly analyzed to drive insights and improve decision making throughout every level of the energy industry. Read on to learn how the following data science use cases in energy are transforming how fossil fuel, electric, and renewable energy companies are optimizing their sector.

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Determining Productivity Drivers

Increased productivity is great. Knowing why productivity has increased is even better. Data scientists and stakeholders can drill down into data collected from energy assets in order to identify what’s hampering or driving productivity, and gain valuable insight into what to divest or invest in. Big data analytics in energy and BI tools help collect and visualize data in a digestible way that makes it easy to identify patterns related to productivity.

An example data science use case: If an oil well’s production has decreased, the root cause could be a dwindling supply, malfunctioning equipment, poor fleet management, inefficient workflows, outdated technologies, or any combination of these factors. If an electric energy supply company’s revenue potential is plummeting, it would be valuable for them to analyze data related to competitor pricing, internal operational inefficiencies, and equipment performance. 

Data science in energy facilitates the ingestion and consolidation of these data streams related to productivity, providing a unified view of how these different factors relate to one another and their impact on productivity. This data can be used to inform response strategies, such as investing in automation technology, replacing old equipment, updating pricing strategies, developing more efficient workflows, and more.

Assessing Land Suitability

Different energy sources require very different kinds of land. An enormous volume of physical and geospatial data is required to determine if a plot of land is suitable for the various energy sources. You wouldn’t build a hydroelectric generator in an area where you can’t build a dam, and you wouldn’t install a wind turbine in an area with very slow air currents, but you can’t be expected to physically visit every potential plot of land to analyze its suitability by hand. Instead, we leverage sensor networks and data science energy sector tools to assess land suitability. Some data science examples include: Biomass: biomass is measured using data from Radar, LiDAR, optical remote sensing, spatial data processing, and image fusion

  • Geothermal: thermal infrared technologies gather surface temperature data
  • Hydroelectric: GIS collect geological and structural mapping data; remote sensors collect basin water drainage pattern data 
  • Natural Gas: uses current and historical data regarding over the counter swap information, volatility, interest and exchange rates, assessed prices and future prices, technical analysis, and geopolitics; remote sensors collect data related to power generation and distribution, production, logistics, geospatial and time-series. 
  • Solar: remote sensing data from geostationary satellites is used to help derive surface solar irradiance from satellite images
  • Wind: data science for wind energy includes remote sensors that collect atmospheric turbulence, wind speed and direction, temperature, moisture, and water vapor data.

Data science and energy tools enable quick and easy ingestion, consolidation, and transformation of vast, static, and cumbersome energy datasets into straightforward, interactive data visualizations that help users glean previously hidden insights.

Brokering Acquisitions

The energy sector is an increasingly competitive landscape worldwide. Energy companies are vying to expand their businesses by acquiring smaller, less-profitable plants and entering new markets in order to enhance their competitiveness. With the variety of energy markets continuing to expand, e.g. metals and mining, chemicals, agribusiness, climate and sustainability, oil, and more, it is crucial for energy companies pursuing market expansion to leverage data science in order to identify the best energy companies to invest in.

Examples of data science benefits in acquisition include: a consolidated view of the full energy-value chain and enable acquisition and divestment professionals to aggregate, view, and analyze energy data from a wide variety of different companies and markets. This data can then be used to economically evaluate asset acquisition metrics in these companies and markets. Data science solutions like energy business intelligence tools and energy analytics help users access and analyze data that can answer questions related to benchmark performance, productivity drivers, market demands and trends, and more. 

Identifying New Assets

Smart grid, sensor networks, and IoT technologies generate an enormous volume of energy data with real-time updates on factors that help data analysts and decision-makers determine how and where various energy options should be placed. 

Data science energy tools help users aggregate and analyze this data in order to assess the profitability of land using massive multi-sourced datasets that represent location, time, landscape, and weather, and determine where their customers should make wind, solar, biomass, hydroelectric, geothermal or other renewable energy investments. Energy data science solutions enable acquisition and divestment professionals to aggregate, view, and apply thorough statistical analysis to the energy data, and economically evaluate asset acquisition metrics in the market.

Visualize & Optimize the Full Energy Value Chain

Data science in the energy sector is crucial to our ability to analyze the full energy value chain.  The energy value chain involves the process of linking specific functions from input to output to increase the economic value of a business through the value chain. This includes data from end-users, utilities companies, and from businesses upstream, midstream, and downstream, for both renewable resources and integrated oil and gas. Each segment has different characteristics and offers different investment opportunities. In analyzing each link in the energy value chain, energy players can identify opportunities, mitigate risk, and improve their business strategy.

Upstream consists of exploration & production (E&P) and oil-field equipment and services companies, and is impacted by complex global economic and political factors; Midstream consists of companies that function to store and transport crude oil, natural gas, and natural gas liquids; and Downstream, which consists of companies that refine crude oil and market the finished product, is impacted by changes in oil prices and environmental regulations. 

Optimizing the full energy value chain requires the use of a data science platform that can provide a unified, big-picture view of how these sectors function and interact, as well as the ability to drill down on a granular level into the workflows of each sector to identify patterns and trends.

Choosing the Right Renewable Energy

Data science for renewable energy source assessment is only going to grow in popularity as the pressure to “go green” intensifies. An increasing number of stakeholders are looking for insights into which renewable companies to invest in, and energy and data science tools and methods provide insight into the four main areas of business that can help determine which clean energy sources to invest in and how to keep those energy assets productive and profitable. Some renewable energy and data science use cases include:

  • Risk intelligence: BI tools use Machine Learning and predictive models to determine risks associated with trading certain energy assets.
  • Demand intelligence: BI tools help monitor energy costs, service availability, downtime, and energy & utility demand and distribution. This current and historical data is used to identify trends and predict energy usage.
  • Asset intelligence: BI tools help track asset usage and efficiency, monitor equipment in real-time for preventive maintenance and replacement, and analyze energy generation and outages.
  • Customer service intelligence: Customers won’t hesitate to tell you where your business is lacking, which provides decision makers with invaluable feedback, but only if they can get through to you. Intuitive BI dashboards will help streamline call center operations and increase efficiencies.

The HEAVY.AI Advantage

Energy is a massive and complex sector with huge potential for growth and optimization, and only the most advanced big data analytics tools can truly harness the power of the vast data streams that energy assets generate. HEAVY.AI’s hardware accelerated analytics delivers greater speed and performance than any legacy system could handle. Instant interactivity coupled with illuminating visualizations of big data drives faster action and better, data-driven decisions. 

Hardware acceleration provides decreased time to value and empowers energy players to: ask the right questions and define objectives, fix inconsistencies and gaps in data, analyze data and form hypotheses about problems, conduct effective demand forecasting, perform fast data discovery, train accurate predictive models, monitor ML models, and create interactive data visualizations that reveal hidden insights and clearly communicate findings to the right people – all at lightning speeds. 

When HEAVY.AI gives data scientists and energy managers the power to explore big data at the speed of thought, the questions you can answer are limitless.