The impact of data science on the energy industry
The science of big data is changing the energy industry, as it is in many other industries, by providing insights into cost reductions in declining markets and enabling oil producers to meet the demands of the market in times of prosperity.
Algorithms, data tools, sensors, Internet of Things (IoT) devices, machine learning, and data mining approaches have all advanced significantly. As a result, it has been shown that big data analytics can give a data-driven approach in:
• Using smart sensor data and machine learning techniques, optimize the heating and cooling system.
• Analyze data usage patterns to optimize the maintenance, efficiency and lifespan of current solar panel technology.
• Enable the development of algorithms to forecast and predict changes in solar and wind conditions. To increase the efficiency of clean energy generation, these systems use data regarding weather, environmental and atmospheric conditions.
• Help develop low-cost solutions for emerging countries by analyzing mobile phone data to predict usage trends and better manage batteries and power sources. To make energy last as long as possible, these predictive models can be used to change the brightness of lights and slow the rate of cell charging.
• Optimize performance and improve equipment maintenance schedules by using satellite imagery and remote sensing technology to improve oilfield production.
• Help regulators make more informed and sustainable energy decisions.
Big data for solar and wind energy management has been a hot topic of discussion. The main problem with wind and solar is that they don’t produce enough energy when natural resources are scarce. During these periods, means such as gas, coal or nuclear energy must be used to make up the shortfall.
Data analysis and computer models can determine highs and lows in energy consumption, as well as when there is a surplus, by collecting consumption data and combining it with other information sensory. These templates can be used for a variety of purposes, including:
• Estimate when and how much fossil fuels will be needed to limit the amount consumed and carbon pollution.
• Determine the best locations for the turbines based on consumption and available resources.
• To reduce energy waste, upgrade and implement a more efficient backup facility.
• Manage certain aspects of the utility industry more efficiently, resulting in cost savings.
Big data analytics and machine learning have huge potential in the clean and renewable energy sector. IT can provide many benefits in terms of improving the future sustainability of our ecosystem. A proven method of innovation is to transfer ideas and methodologies between sectors. According to Francisco Sanchez, President of Houston Energy Data Science, “The energy industry has recently begun to integrate the medical field’s survival analytics approach.” Survival analysis is a statistical method used in medicine to assess the survival rates of patients based on their condition, therapies, and other factors. This approach has been applied to field equipment in the oil and gas industry.
“Through monitoring and modeling, survival analysis is used to estimate maintenance needs for field equipment such as compressors,” says Sanchez. Instead of shutting down an oil well for three days to repair damage caused by equipment failure, he says preventative action based on data science can reduce downtime to just one day. A day of free time is priceless. At current prices, one day’s production at a small facility – 1,000 barrels of oil – equals $30,000 in revenue.
“It is essential to start with the business challenge before diving into the technologies and approaches,” says Francisco Sanchez. Forecasting production, improving field efficiency, and understanding geological activity are all common business concerns in the energy industry. “Big companies like BP and Halliburton have used data science methodologies before. “I see a huge opportunity for smaller companies with less sophisticated data to win by bringing in one or two expert data scientists,” says Sanchez. .
“In the oil and gas industry, you have a lot of data to process, and putting it all together takes time.” I have 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 another system. “Tools like Hadoop and NoSQL are needed to bring all this data together,” Sanchez explains.
“In terms of specific tools, it will be determined by the complexity of the problem. I recommend checking out machine learning tools if you’re working on a problem with more than 50 variables. “One option to look into 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 visualization tools for presenting data.
Consulting firms and analysts have traditionally given value to the sector with their specific knowledge, and the same is true in the case of analytics and energy. Another way to create value with data science is to organize and display data in a usable way.
“As a data analyst, I spend the majority of my time visualizing drilling and drilling performance.” I have developed data collection techniques that allow me to compile hundreds of data sources into ordered packages for display and performance evaluation. My company then sells this material at a price higher than the market standard. Since many companies lack in-house capabilities for such operations, the market pays a premium for explanatory data visualizations, according to Graham Eckel, a former analyst at Precision Drilling in Calgary, Alta.
“There are still plenty of data opportunities in the energy sector.” It starts with implementing systems and processes to collect, cleanse, and store data. One way to get started is to hire a data scientist to design the architecture and oversee the execution. “Once you have that in place, you can start generating predictive insights,” Eckel adds.
“We believe that without real-time visibility, misunderstandings will arise. When it comes time to sell that capacity in the markets, the network, if you don’t know what technologies are available in the market, what the capacity of your property is, if you don’t know how much money you’re going to pour into the solar, you are in panic. It is not a simple or straightforward experience. Says Mahmudul Hasan, Founder and CEO of Nexergy, a New York-based cleantech startup. He adds, “As a result, we place a high priority on real-time data at Nexergy. We have combined technologies to allow us to broadcast data streams and ingest databases of time series data, and we have applied it to the energy sector. Big data is influencing the future of renewable energy. Weather forecasting using data science could be beneficial for renewable energy sources like wind and solar. It can be used to help homeowners decide whether or not to go solar by estimating costs and savings. It can also be used to streamline day-to-day management and operations to help new renewable energy companies attract investors. Renewable energy companies can use advanced analytics to gain valuable insights into how to better manage the system and predict how much energy can be used in the power grid or stored for later use.
Hasan says Nexergy intends to provide households with all-in-one solutions to decide on solar installation, upgrades and maintenance. “Nexergy calculates solar energy savings by estimating the cost of solar installation and upgrades, as well as financing options, based on roof size and shape, shaded roof areas, local weather, local electricity rates, solar costs and expected 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 weather data, EPA GHG equivalencies, SLED local and state energy data from the Department of Energy and Google Maps. When a user enters their address and some basic information into the Nexergy system, Nexergy pulls real-time data from the embedded resources and shows it to the user for free. He also claims that Nexergy is working on integrating real-time energy prices based on user geolocation and developing a system that allows a microgrid or homeowner to track costs. energy, savings and carbon emissions. “A microgrid operator/homeowner, for example, can use Nexergy data to decide the best time, method and technology to install or replace solar panels, as well as make decisions related to energy. All of these applications require a significant amount of data collection and processing. Hasan explains.
Big data and data analytics have been used in the energy industry for years to improve generation and service offerings such as utilities. More data scientists will be needed to optimize the performance of solar and wind farms as clean energy becomes more profitable. By 2050, renewable energy is expected to represent half of all energy sources. When it comes to clean energy, other technological advances such as battery technology and long-distance power exchanges will add more responsibilities for data scientists to keep up with. This highlights the relevance of big data to optimize renewable energies and transform them into a future source of energy. Data science will be in high demand for sustainable energy in the near future as well as in the long term.