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Introduction

Across the globe, utility companies are no longer simply providers of cheap energy to their consumers. As more economies switch to “smart” grids, the energy industry is gradually adopting digital technologies to improve the efficient use of energy and quality of service. For power generation companies, data analytics is a valuable tool to manage energy consumption at affordable costs.

According to Mordor Intelligence, the Big Data Analytics market is set to grow at a CAGR of 13.15% between 2023 and 2028. In addition to its role in the fossil fuel industry, data analytics is also a necessity in renewable solar plants.

As more electricity grids and solar panels get decentralized, data analytics enables utility companies to manage their infrastructure and predict any change in power demands. Further, big data enables the rapid development of new business models & services in the energy sector.

In this blog, we shall look at 5 top applications of data analytics in the energy industry.

5 applications of Data analytics in the Energy sector

Data analytics can tap into the increasing data volumes originating from “smarter” infrastructure devices. With data analytics, energy companies can extract strategic insights that can improve energy productivity. Matt Schnugg of GE Power Digital says, “Data is the linchpin of our collective energy future.”

Here are the top applications of data analytics in the energy industry:

1. Detecting and Predicting Power Outages

Regular breakdowns in energy equipment can lead to major power outages and frustrated consumers. Besides, energy companies spend a lot of money on restoring failed equipment or on new assets. Along with IoT-connected sensors, data analytics tools can proactively monitor energy systems for any potential issues.

Here are some of the capabilities of a “smart” energy system:

  • Accurate prediction of weather conditions on the power equipment
  • Possible detection of power outages in specified areas or events
  • Identifying the probable cause of a power outage

By detecting any disruption in the early phase, data analytics tools can effectively prevent any major failure.

2. Determining Weather Patterns

In the renewable energy sector, weather patterns are an important factor in predicting energy supply. As we know, solar and wind energy are among the widely used forms of renewable energy. However, these energy sources are largely dependent on the ample supply of sunshine and wind flows.

By combining big data with predictive analytics, renewable energy companies can leverage real-time weather data to furnish accurate data patterns. Besides, weather forecasting models enable energy companies to accurately predict natural disasters like floods and heavy rains. For future studies on climate change, data models can collect valuable data points related to “average rainfall received in a particular region.”

3. Managing Dynamic Energy Requirements

Energy management systems are an innovative approach to managing energy loads dynamically. These systems comprise a network of “smart” end-user devices, distributed energy sources, and an integrated architecture for smooth communication.

This form of energy management addresses all concerns about growing energy demands and obtaining power from distributed fossil and renewable sources. Besides, it can also address energy-related challenges like managing cost efficiency and reduction in energy demands.

Using Big Data analytics, energy providers can empower dynamic energy systems and optimize the energy distribution to their consumers. By processing large volumes of data, data analytics tools can estimate performance levels and provide intelligent recommendations.

4. Maintaining Power Equipment

Energy companies invest thousands of dollars into maintaining their power grids and equipment. Despite all that, failures can still happen, leading to blackouts and losses.

Data-driven predictive maintenance is an accepted method to gauge the current health of critical power infrastructure. For instance, in connected solar panels and power grids, utility data (from multiple data points) can provide insights into which component requires urgent repair or replacement.

Moreover, energy providers can leverage predictive maintenance to determine the remaining life of the equipment or when the next failure is likely to occur.

5. Reducing the Cost of Energy Production

Over the years, renewable energy including solar power has received more support from the industry. This has significantly helped in reducing the cost of producing more solar power. Using the right data insights, renewable energy companies can optimize energy production and accurately predict energy supply and demand.

Additionally, the cost of installing and operating solar power plants continues to fall across the globe. The U.S. Department of Energy has announced its plan to reduce the cost of producing solar energy by 60% by the end of this decade. Other studies conclude that advanced data analytics can improve energy savings by 5-7.5%.

By leveraging big data analytics and predictive analytics, renewable energy producers can produce more energy without incurring any extra infrastructure costs.

Conclusion

Be it in the traditional or renewable energy domain, companies need to leverage big data to improve production efficiency, provide a better customer experience, and reduce costs. With the right data-driven insights, they can improve their decision-making process and transform business outcomes.

Pratiti Technologies offers a host of customized data analytics solutions to solar and renewable energy producers. With our analytics solutions, organizations can develop a sustainable business model in any area of business operations.

Looking for the right data analytics solutions in the energy industry? Let us help. Contact us today!

Nitin
Nitin Tappe

After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

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