The role of big data analytics in Energy & Utilities

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Nokia has partnered with the State Grid Corporation of China for data transmission to allow SGCC to expand network coverage to more power stations and business offices, while also increasing capacity and flexibility.

Data has always been a critical component of E&U’s operational processes. However, with the introduction of new data sources and the subsequent increase in the volume of generated data, big data analytics is assuming priority.

Technologies such as Phasor Measurement Units (PMUs), Advanced Metering Infrastructure (AMI), smart meters, and Geographic Information Systems (GIS) are now being employed for data transmission, storage, and correlation.

As E&U is responsible for fulfilling the power and energy demands for the other industries, it is the lifeline for every other sector. Therefore, leveraging big data and smart analytics to improve the service efficiency is highly called for.

Jeff Ressler, Executive Director at Clean Power Research, was quoted as “The unprecedented interconnectedness of systems and available computational power through the cloud are allowing new system-wide data analytics applications. The era of siloed utilities is over, and executives are working on creating high fidelity, high quality data structured to be used throughout the company.”

Let us understand the role of big data analytics in Energy & Utilities in detail:

Practical use cases of big data analytics in E&U

The primary use cases of big data analytics in the Energy & Utilities sector are:

  1. Prediction, detection, and prevention of power outages

A power outage may cause an entire country to come to a halt, like the Northeast blackout of 2013 that affected over 45 million people in the U.S. Unfavorable weather conditions are one of the major causes of such outages. Still, E&U companies are building smarter infrastructure and sensors to enhance predictability and prevent such outage scenarios.

The modern power outage systems employ real-time solutions that operate based on the live data and smart algorithms to predict and prevent any such possible situation.

These systems are capable of predicting the impact of any near-time asset values on the network grid, possible outages caused due to smart meter events, region-specific outages, and more.

  1. Smart load management

To efficiently management energy load, the E&U companies need to strategically and intelligently balance energy demand with optimal power supply in a given time period. Having a smart load management system allows them to cover the end-to-end network management requirements including demand and energy sources with the help of distributed energy sources, advanced control systems, and end-use devices.

All of the components within the management system generate data. By applying big data analytics, the companies can accurately make decisions regarding their power planning and generation, energy load, and performance estimation.

  1. Preventive asset management

E&U is an asset-intensive industry and relies heavily on the optimal performance of their equipment and network infrastructure. Failure of these assets may cause serious power distribution challenges and consequently, depleted consumer trust. Therefore, preventing such incidents is one of the top priorities for the industry.

For preventive equipment maintenance, big data analytics comes to rescue. The assets are integrated with smart sensors, trackers, and data solutions that relay real-time information to the center. The   data gathered can then be processed and analyzed to identify any potential issues with the equipment maintenance, allowing a proactive handling of the situation.

  1. Greater operational efficiency

Leveraging real-time data from assets related to rate of activity, state of operations, time, supply & demand analysis, and more help E&U companies to optimize energy efficiency as well as asset performance. The big data analytics applications enable them to enhance reliability, capacity, and availability of their network assets by continuous monitoring of cost and performance.

Big data testing for the Energy & Utilities sector

The power outage of 2013 that we talked about earlier was caused by a software bug in the deployed modern grid solutions. With the introduction of highly advanced infrastructure to deliver reliable and continuous energy, E&U companies also need to consider the challenges that may arise due to faulty software technologies.

As so much relies on real-time transmission and analysis of big data across the grid, it is high time that the industry realized the criticality of big data testing as well as end-to-end testing across their grid.

A top-tier provider of home comfort systems needed a low-cost load generation environment to analyze base-line response time on concurrent usage. By employing a cloud-based load generation strategy, they optimized response time by 30%, hardware performance by 25%, & server performance and scalability by 20%. Read the complete story.

How can we help

Cigniti understands the testing needs of the E&U landscape that is undergoing a massive transformation from existing systems to better ways of consumption, production, and distribution.

Having delivered comprehensive testing services to E&U organizations in US, UK, and Europe, we provide complete test coverage, test acceleration, and tool agnostic test frameworks.

We leverages our experience of having tested large scale data warehousing and business intelligence applications to offer a host of Big Data testing services and solutions.

With capabilities that enrich QA initiatives across change management, compliance programs, and executive monitoring, our services strengthen the global E&U ecosystems with precision and high standards of QA to overcome business concerns across production, transport, distribution, consumption measurement, and billing.

Schedule a discussion with us today.

Author

  • Cigniti is a Global Leader in Independent Quality Engineering & Software Testing Services with offices in US, UK, India, Australia, and Canada.

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