The Impact of Data Science on the Energy Industry


Big data science is altering the energy industry, as it is in many other industries, by providing insights into cost reductions in down markets and allowing oil producers to respond to market demands in boom times.

Algorithms, data tools, sensors, Internet of Things (IoT) devices, machine learning, and data mining approaches have all advanced dramatically. As a result, it has been demonstrated that big data analysis can give a data-driven approach in:

• Using smart sensor data and machine learning techniques, optimise the heating and cooling system.

• Analyze data usage patterns to optimise maintenance, efficiency, and life span of current solar panel technology.

• Allow algorithms to be developed to foresee and predict changes in solar and wind conditions. To increase the efficiency of clean energy production, such systems make use of data regarding weather, environment, and atmospheric conditions.

• Assist in the development of low-cost solutions for emerging countries by analysing data from mobile phones to predict usage trends and better manage batteries and power sources. To make energy last as long as feasible, these predictive models can be used to modify the brightness of lights and slow down the rate of cell charging.

• Optimise yield and enhance equipment maintenance schedules by employing satellite imagery and remote sensing technology to improve oil-field production.

• Assist regulators in making more informed and sustainable energy-comprehensive decisions.

Big data for solar and wind energy management has been a hot topic of discussion. The main issue with wind and solar is that they do not produce enough power when natural resources are scarce. During these periods, methods such as gas, coal, or nuclear power must be used to make up for the deficiency.

Data analysis and computational models can determine the highs and lows of power usage, as well as when there is a surplus, by collecting data on usage and combining it with other sensory information. These models can be used for a variety of purposes, including:

• Estimate when and how much fossil fuels will be required in order to limit the amount consumed and carbon pollution.

• Determine the best sites for turbines depending on consumption and available resources.

• To reduce power waste, improve and implement a more efficient backup facility.

• Run aspects of the utility industry more efficiently, resulting in cost savings.

Big data analytics and machine learning have enormous potential in the sector of clean and renewable energy. There are numerous advantages that computer science may provide in terms of improving the future sustainability of our ecosystem. A tried-and-true innovation method is transferring ideas and methodologies across sectors. According to Francisco Sanchez, president of Houston Energy Data Science, “the energy industry has lately started to incorporate the survival analysis approach from the medical field.” Survival analysis is a statistical method used in medicine to assess patient survival rates depending on their condition, therapies, and other factors. This approach has been applied to field equipment in the oil and gas industry.

“Through monitoring and modelling, survival analysis is utilised to estimate the maintenance requirements for field equipment such as compressors,” Sanchez explains. Instead of shutting down an oil well for three days to repair damage caused by equipment failure, he claims that preemptive data science-enabled action may cut downtime to only one day. A day’s worth of free time is priceless. At current pricing, a day’s production at a small facility – 1,000 barrels of oil – equals $30,000 in revenue.

“It’s critical to start with the business challenge before diving into technologies and approaches,” Francisco Sanchez argues. Predicting output, enhancing field efficiency, and understanding geological activity are all common commercial concerns in the energy industry. “Big companies like BP and Halliburton have already used data science methodologies. “I see a huge possibility for small businesses with less sophisticated data to win by bringing on one or two expert data scientists,” Sanchez says.

“In the oil and gas industry, you have a lot of data to deal with, and putting it all together takes time.” I’ve seen projects where some data is stored in Oracle databases, while drilling data is stored in other databases, and economic and seismic data is stored in yet another system. “Tools like Hadoop and NoSQL are required to bring all of this data together,” Sanchez explains.

“In terms of specific tools, it will be determined by the problem’s complexity. I recommend looking at machine learning tools if you’re working on an issue with more than 50 variables. “One option to examine is Random Forest, which is provided by Salford Systems,” he says. R and Python are included in the data science toolset for additional applications. He continues, “Tibco and Tableau are useful visualisation tools for presenting the data.”

Consulting firms and analysts have traditionally given value to the sector through their specific knowledge, and the same is true in the case of analytics and energy. Another method to create value with data science is to organise and show data in an usable way.

“As a data analyst, I spend the majority of my time visualising rig and drilling performance.” I’ve developed data collection techniques that allow me to compile hundreds of data sources into tidy packages for display and performance evaluation. My company then sells this material at a higher price than the market norm. Because many firms lack in-house capabilities for these operations, the market pays a premium for explanatory data visualisations, according to Graham Eckel, a former analyst with Precision Drilling in Calgary, Alberta.

“There are still lots of data opportunities in the energy business.” It begins with the implementation of data gathering, cleansing, and storage systems and processes. One method to get started is to hire a data scientist to design the architecture and supervise the execution. “Once you have that in place, you can start generating predictive insights,” adds Eckel.

“We believe that without real-time visibility, misunderstanding will arise. When the time comes to sell that capacity to the markets, the grid, if you don’t know what technologies are available in the market, how much is the capacity of your property, if you don’t know how much money you are going to pour on the solar, you’re in a panic. That is not a simple or straightforward experience.” Says Mahmudul Hasan, Founder and CEO of Nexergy, a cleantech startup based in New York.  He adds, “As a result, we place a great priority on real-time data at Nexergy. We’ve combined technologies to allow us to stream data streams and ingesting databases of time series data, and we’ve applied it to the energy sector. Big data is influencing the future of renewable energy. Weather forecasting using data science could be advantageous to renewable energy sources like wind and solar. It can be used to assist homeowners in deciding whether or not to go solar by estimating costs and savings. It can also be used to streamline management and day-to-day operations in order to assist new renewable energy firms in attracting investors. Renewable energy companies can use cutting-edge analytics to gain valuable insights into how to better manage the system and forecast the amount of energy that can be used in the power grid or conserved for later use.”

Hasan claims that Nexergy intends to give all-in-one solutions for households to decide on solar installation, upgrades, and maintenance. “Nexergy calculates the solar savings by estimating the cost of solar installation and upgrades, as well as financing possibilities, depending on the roof size and shape, shaded roof areas, local weather, local power rates, solar costs, and projected incentives over time. We provide information on solar potentials, incentives, legal and regulatory requirements, and geolocation based on the 2010 United States Census, National Renewable Energy Laboratory meteorological data, EPA GHG Equivalencies, Department of Energy SLED State & Local Energy Data, and Google Maps. When a user enters his address and some basic information into the Nexergy system, Nexergy pulls real-time data from integrated resources and shows it to the user for free.” He also claims Nexergy is working on integrating real-time energy prices based on the geolocation of the user and developing a system that allows a microgrid or homeowner to track solar costs, savings, and carbon emissions. “A microgrid operator/homeowner, for example, can utilise Nexergy data to decide the best time, method, and technology to install or replace solar panels, as well as make energy-related decisions. All of these applications need a significant amount of data collection and processing.” Hasan explains.

Big data and data analytics have been used in the energy business for years to improve production and service offerings such as utilities. More data scientists will be needed to optimise the performance of solar and wind farms as clean energy becomes more profitable. By 2050, renewable energy is expected to account for half of all energy sources. When it comes to clean energy, other technological advancements such as battery technology and long-distance energy exchanges will add more responsibilities for data scientists to keep track of. This emphasises the relevance of big data in optimising renewable energy and transforming it into a future energy source. Data science will be in high demand for sustainable energy in the near future as well as in the long run.

Jacob Musser
Jacob Musser
Jacob Musser is an independent cleantech analyst based in the United States. He can be contacted at jacobmusser[at]


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