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Energy and Utilities
Case Study

Customer Profile

The customer is a utilities company that serves more than 37 million clients in over 20 countries. Its goal is to supply safe, clean, and affordable electricity to their customers in a sustainable manner. The company’s dedicated affiliate that worked with 47Lining focuses on renewable energies, including wind, solar photovoltaic, and storage.

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To keep track of system health, each of the company’s solar panels and wind turbine generators are equipped with sensors that collect data on power output, temperature, wind direction and speed, irradiance, and other relevant conditions. This fleet generates more than 3 million data streams coming from operational assets in 15 countries across 5 continents, with updates to each stream occurring up to once per second. Prior to leveraging Amazon Web Services (AWS), this data was collected and stored in a variety of manners – including its enterprise time-series historian system – using on-premises hardware in multiple silos, making it difficult and expensive to scale.

The company wanted to quickly and cost-effectively establish a repository for all of their time-series data. Additionally, they wanted deeper insight into how its assets operate. This insight would help them provide better services at a lower cost point with more transparency by accounting for how customers expect energy to be delivered together with associated risks.

To achieve greater stability and visibility, the company determined to push the data contained in their enterprise time-series historian systems to a centralized time-series data lake. With these aspirations in mind, they decided that a cloud-based architecture was the optimal solution.

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47Lining had executed a prior proof-of-concept (PoC) with the company to demonstrate the value of an AWS-based data lake for industrial process data from other supervisory control and data acquisition (SCADA) systems. Based on the PoC, and their work with organizations in the oil and gas, mining, and manufacturing industries, 47Lining had already solved for many of the common requirements the company had, including methods for deriving richer insights from enterprise time-series historian system data on the cloud. This experience enabled 47Lining to build the AWS Industrial Time Series Connector Quick Start, a “data bridge” that connects enterprise time-series historian system industrial time-series data to AWS.

Since 47Lining had already built the AWS Quick Start, they were able to immediately begin assessing the company’s more specific requirements. The customer’s on-premises environment relied on Microsoft SQL Server Standard Edition to run queries on their enterprise time-series historian system. Maintaining this workflow was an imperative as they transitioned to the cloud.

To get their solution ready for production, they built out a new industrial process data lake on AWS, leveraging Amazon Simple Storage Service (Amazon S3) as a central repository for all the sensor data coming from their wind turbines and solar panels.

Thanks to the close relationship between the company, 47Lining, and AWS, the system was rapidly implemented and functional within 12 weeks. Through this process, 47Lining also helped the company get familiar with running their new cloud-native system, empowering them to confidently operate it post-implementation.

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Now that the company has their enterprise time-series historian system data bridge in place, they can leverage the AWS global infrastructure to easily transfer data from their solar panels and wind turbines into the cloud in a centralized framework, with much greater transparency and stability than on-premises. None of the institutional knowledge they have developed over several decades in operation has been compromised—they’re still able to use the BI tools they’re familiar with and standard SQL. Their AWS data lake empowers them to drive insights that help reduce costs, drive new revenue, and provide greater transparency into their valuable assets.

What's Next

The company is continuing to investigate new ways to tap into the broad portfolio of AWS big data and analytics services to better meet their customers’ changing expectations and remain a leader in the renewable energy space.

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