How Big Data Analytics is Transforming Energy Industry

Energy is the fountainhead of human development.With an increasing demand for energy coupled with depleting sources energy sector has started adopting technologies that improve production, reduce transmission losses, and improve efficiency in utilization.

The advent of smart grids, smart meters,other advanced distribution solutions has increased data generated by several folds.The energy sector has woken upto big data analytics. It is making strides in harnessing the power of data analytics and advanced analytics to garner insights that drive their strategic decisions on investments, operations, and risks assessment to improve bottom lines.Analytics onreal-time data has helped industries to optimize energy production, weather forecasting, building-energy management, demand forecast, and preventive maintenance for equipment.Utility companies, grid operators, transmission companies, and other electric industry entities have long been users of predictive analytics to reduce downtime and maximize revenues.The fast depleting conventional energy source has forced energy industries to embrace conservationon one side and look for alternate energy sources on the other. Data analytics aidsthe implementationof energy conservation and renewable energy utilization strategies. Energy companies, in collaboration with utility companies, can exploit data analytics to predict energy demands to optimize demand-supply conundrum. The use of smart grids and smart meters,in conjunction with real-time analytics, can reduce transmission losses and enhance reliability.

Renewable Energy

Rising global awareness and commitment towards 100% renewable energy has forced energy industries to look at renewable energy sources as a viable option for energy generation, even though renewable energy sources like wind, water and solar are inexhaustible, intermittent, unpredictable. It isand posesdifficulty to bank energy generated, andtotal reliance on these renewable energy sources without proper management is rife with challenges.

Predictive analytics and machine learning capabilities of AI on historical data along with satellite data have been used to make accurate forecasting of weather conditions well in advance. This has helped both solar and wind energy plants to increase their production and also manage grids based entirely on renewable energy. In order to make energy production, viable solar and wind farms have to be huge and equipment installed widely apart. As a result, operation and maintenance become difficult. However, data analytics has been effectively used to streamline processes and support of vast energy farms. The ability to forecast energy generation based on historical data on performance, weather, and other parameters has enabled energy companies to negotiate better power purchase agreement terms and also estimate the quantity of energy that can be redirected into the power grid.

Utility analytics

Utility companies have the most unenviable job in the world of keeping the lights burning and machines running all round the year. Despite being an essential service, they are highly regulated, and their operations are complex, making an adaption of modern technology slow. The advent of smart meters, intelligent distribution grids, exchanges that support instant buying and selling has propelled the industry towards digitization.Utility companies generate a huge amount of data from sensors, transmission and distribution networks,and substations. Data analytics has been successfully implemented by these companies to gain deeper insights to improve operational efficiencies and manage varying energy demands.

AI is playing a significant role in ushering data analytics into the utility industry. Using IoT,and AI on real-time data from IoTassets, SCADA, and customer data, utility industries have adopted predictive analytics to forecast demand and improve operations. Predictive analytics has helped utility companies in:

  • Implementing preventive schedules: this ensures reduced distribution loss andfewer blackouts.
  • Demand forecasting helps to estimate power requirements using power consumption patterns and power generating capacity to prevent power shortage or massive cost of power banking.
  • Enhancing customer experience: can be used to identify customer-specific usage data to craft tailor-made tariff plans.

The use of new real-time data streams and predictive analytics will give utility companies intelligent decision-making capabilities to unearth insights to identify operational risks and opportunities

Industry experts are foreseeing the use of data analytics to give rise to decentralized smart networks that allow peer-to-peer energy transmission soon

Building Automation analytics

As the quest for efficient systems continues especially in the energy sector, individual building one of the significant energy guzzlers becomes hard to be ignored. Smart buildings incorporate HVAC systems, lighting systems along with security and surveillance systems. At the heart of a building automation system are numerous sensors and transducers, and large volumes of datafrom these are analyzed to gain insightful data that can be used to optimize energy consumption and improve efficiency. IoT for building automation analytics enables the data collection from dispersed sources and converts data into actionable insights toidentify cost-saving opportunity and system inefficiencies for better monitoring and control.

Some of the otherbenefits of building management system analytics include:

  • Reduced running costs and extended life of equipment
  • Implementation of proactive maintenance schedules and fault detection
  • Improved tenant comfort
  • Opportunity to continuously monitoring even remotely

Oil and Gas analytics

Oil prices drive significant economies in the world.  A vast and complex industry like oil and gas can undeniably benefit from data analytics, making it efficient and profitable. Conventionally tech-driven highly complex operations of production and processing facilities are monitored and managed by simulation tools and SCADA systems. Innumerable sensors generate vast volumes of data with advanced analytics companies gain valuable insights that can help in reducing risk and increasing the accuracy in decision making in a cost-effective way.

Fouling impedes fluid flow, accelerates corrosion, and increases the pressure drop across pipeline networks. We have developed Deep Learning models using Convolution Neural Networks to detect specific patterns in the pipe and joints for the detection of leaks and faults.

Oil drilling is a continuous processthat needs the availability of machines for a long duration at their efficient best. Predictive analytics has been successfully employed for continuous monitoring of machines and in the implementation of a preventive maintenance schedule to minimize breakdowns and downtime.Advanced analytics using Machine Learning and custom-built algorithms has proven indispensable in production and operation management in identifying bottlenecks and streamlining the process. Highly labor intensive oil and gas industry has efficiently used technologies like robotics powered by AI and ML to automate repetitive tasks and in exploration activities.

Power plants analytics

Given the intricacy of power plant operationengineers have striventooptimize efficiency and power output of the plant and ensure long term operation. Conventionally power plants have fallen back on process control systems like DCS, SCADA, and PLC or other operations management systems, but as the industry caught up with technology adaptation improved process instrumentation and monitoring software have been in use.In the fast-changing and competitive environment,Power plants have successfully adopted data analytics for insights that will help them take timely and accurate decisions to improve plant operational efficiency, optimize costs, increase availability, reduce downtime and enhance capacity while operating the plant in a safe & environment-friendly atmosphere.

Power plants are harnessing predictive analytics to improve the operation and maintenance of virtually every major equipment and essential process in a power plant by usinga preventive maintenance program that reduces machine downtime. Data analytics based on Machine Learning and algorithmshas been used to monitor most of the power plant components and implement continuous monitoring of critical equipments like cooling fans using IoT and automated analysis.

Energy trading analytics

Commodity trading is a complex activity with many variablesthat are both internal industry-specific and external market-specific. The energy, with its continued high demand and varied sources, brings its own complexity into trading. With energy companies working towards increasing profitability and narrowing margins,energy trading companies are looking towardsbig data to gain insights on understanding relevant market forecasts discrepancies in the market to predict future demands. Predictive analysis has been successfully employed by traders worldwide to steer their day to day trading activities.

Sophisticated analytics are enabling energy companies with deeper insight that has helped them make strategic business decisions for sustainable development and meet the energy demands of the 21st century.

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