Realizing Your Clean Energy Goals with Accenture’s Data-Led Transformation on AWS

By Alex Gilden, Manager, Applied Intelligence – Accenture
By Daniel van der Have and Sanjay Mamidipalli, Managing Directors – Accenture
By Arun Misra, Sr. Manager, Applied Intelligence – Accenture
By Soonam Kurian, Sr. Solutions Architect – AWS

Accenture

There has been an increased emphasis on decarbonizing the energy value chain in recent years. Accenture’s North American utilities industry clients are also navigating a higher frequency of state and federal regulations mandating near- and long-term emission reduction targets.

With the latest crop of legislation, Accenture has observed its utilities clients unlocking crucial funding for energy efficiency, electric vehicle (EV) infrastructure, energy storage, and clean jobs initiatives, as well as their underlying analytics platforms. A robust cloud-hosted analytics backbone is instrumental to delivering on clean energy commitments.

In this post, we unpack how utilities are successfully embracing Accenture’s data-led transformation (DLT) and leveraging accelerators powered by Amazon Web Services (AWS) to reach their business objectives and meet regulatory obligations.

Accenture is an AWS Premier Tier Services Partner and Managed Cloud Services Provider (MSP) that provides an end-to-end solution to migrate to and manage operations on AWS. Accenture also holds AWS Competencies in Energy and Data and Analytics, among others.

Where is the Analytics Adoption Gap?

While utilities have historically been rich with data from customers, programs, and assets, Accenture’s clients often manage data in siloes. Source data can also be disorganized, with deficiencies in defined quality assurance and quality control processes.

Furthermore, with a lack of consistent IT spend and myriad security considerations, many utilities are on the early side of their on-premises to cloud migration—oftentimes with hesitation to commit.

This can result in a few key gaps:

• Regulatory reporting risk around utility program performance, outage mitigation, and general returns on operating and capital expenditures.
• Lost opportunity for the business to capitalize on “in plain sight” and “hidden” insights within first-party data.
• Struggles with fully benefitting from the AWS economies of scale principle in the six advantages of cloud computing.

Without the proper foundation of data warehousing, processing, and governance tools, utilities face risk on important business imperatives. This includes timely and accurate regulatory reporting, carbon-footprint tracking, and routine capture of data-driven insights for decision-making based on artificial intelligence (AI) and machine learning (ML) outputs.

In the following sections, we’ll show how AWS data lake architecture, scaling, extract, transform, load (ETL) orchestration, modeling, and end-user reporting services can help close this gap.

Data-Led Transformation (DLT)

Accenture is addressing utilities clients’ people, process, and technology gaps with data-led transformation (DLT). This is a powerful asset within the Applied Intelligence practice of Accenture. The suite of capabilities includes a combination of core AI/ML and insight factory analytics services, as well as specialized Solutions.AI accelerators.

Figure 1 – DLT utilities industry solutions overview.

DLT is disruptive at its core. The capability is built and deployed by thousands of Accenture Applied Intelligence (AAI) practitioners globally with coordination from data science, data engineering, and functional industry subject matter expert (SME) workstreams.

Accenture is collaborating with clients on next-generation utilities solutions with a comprehensive value proposition to the C-Suite, enterprise IT, and respective lines of business. This includes tackling challenging client problems, like how to leverage data and analytics to redefine customer care, reduce cost for meter to bill, and optimize field service and workforce management.

Accenture is also focused on helping clients increase revenue with new energy experiences and business models like distributed generation and microgrids.

DLT Architecture for Utilities

With any analytics-focused platform or suite of services, architecture and core use cases are fluid and mature as clients’ capabilities and expectations grow.

The figure below details a successful AWS-hosted data lake analytics environment replicated with multiple utility clients that are ambitious about their respective cloud journeys.

Figure 2 – Illustrative DLT in utilities solution architecture.

In this common example, the data lake is divided into three zones:

• Ingestion zone: Various first- and third-party datasets for the utility are ingested via AWS Transfer Family, Amazon Kinesis, and AWS Data Pipeline depending on source type, cadence, and any pertinent energy regulatory requirements.
• Curation zone: Accenture-built AWS orchestration tools running on AWS Step Functions, AWS Glue, and AWS Lambda support clients’ IT bandwidth with fully automated warehousing and ETL workflows.
• Analytics zone: Data engineering, data science, and data visualization experts leverage Amazon SageMaker, Amazon QuickSight, and Amazon Redshift for AI/ML modelling and critical end-user dashboard use cases.

Specifically, the Accenture ARTEMIS orchestration tool empowers utilities with cost-effective and high performance storage on Amazon Simple Storage Service (Amazon S3), with all the classic underpinnings of built-in security, data governance, and key management via the suite of AWS services.

Figure 3 – DLT AI/ML workflows for utilities.

The figure above highlights a more granular architecture of DLT utilities data science practitioner workstreams on AWS. This is important for utilities still weighing the talent considerations of bolstering in-house data science capabilities.

Accenture has observed that clients frequently prefer and trust modeling work that can train, tune, and redeploy with triggers like Amazon CloudWatch or Amazon EventBridge for high performance deployment with minimal analytics staff oversight and churn on bug fixing.

Lastly, DLT-focused cloud architecture will always lean toward efficient deployment. Accelerators in play with utilities clients will employ CI/CD frameworks to emphasize highly repeatable infrastructure as code (IaaC) principles within AWS.

Services like AWS CloudFormation and AWS CodePipeline are instrumental in aiding utilities with hastened environment stand-up timelines and the resulting cost efficiencies for development.

DLT Use Cases for Utilities

Let’s highlight select data-led transformation use cases that empowered Accenture clients to leverage AWS to charge towards clean energy goals.

Customer Propensity and Segmentation

Like other industries, many utilities are on a years-long journey to get a succinct and effective single view of their customers. Accenture’s DLT capabilities enabled clients to appropriately segment residential, commercial, and industrial customers by their usage profile, rate category, meter type, housing square footage, building age, and heating/cooling type.

Figure 4 – Energy billing and usage segmentation.

With a comprehensive DLT-led customer analytics record (CAR) built, Accenture is able to supply utilities with propensity models on how best to target or cross-market to customers with energy efficiency offerings to help lower their bill, energy consumption, and emissions profile.

Pertinent regression techniques, forecasts, and next-best action customer insights are built cost-effectively in Amazon SageMaker notebook instances and visualized by clients in interactive Amazon QuickSight dashboards.

CAR-oriented solutions are “table stakes” for clients looking to get real-time insights and actionable intelligence on their customer base in order to drive a significant amount of natural gas therms and electricity Mwh savings.

Electric Vehicle Charging Usage Segmentation

To satisfy the most current regulatory board orders concerning transportation sector decarbonization, Accenture practitioners and utilities have co-created DLT capabilities on electric vehicle (EV) adoption and charging.

A prime use case is to learn about who is charging their EV when, where, and why for grid resiliency. These insights help utilities EV program leads with asset management and transmission and distribution cross-departmental communication to better quantify demand response load ramifications of connecting thousands—and eventually hundreds of thousands—more EVs on the grid annually.

Figure 5 – Electric vehicle charging usage clustering.

The above K-Means Clustering scikit-learn ML model is trained, tuned, and deployed autonomously through Amazon SageMaker. The model enables clients to better depict their customer’s EV charging habits in 15-minute increments throughout the week.

Patterns are automatically detected and clustered around who charges at home throughout the day vs. exclusively at night, at work, on the road, or during off-peak credit hours.

Going forward, the DLT EV usage pattern segmentation models can be deployed for ecommerce delivery and eSchoolbus fleets, and more.

Accenture’s Skills.AI Workforce Talent Platform

An important DLT utilities use case that is tangential to those focused on lowering emissions intensity is that of clean energy jobs. At the federal and state level, Accenture is seeing a decisive uptick in funding opportunities to upskill, retrain, and ultimately staff qualified resources in these roles.

The cloud-hosted Skills.AI platform by Accenture is already deployed as a trusted partner for utilities clients to algorithmically parse resumes, match candidate skills with open opportunities, and provide hyper-personalized recommendations on talent and skilling.

Figure 6 – Accenture Skills.AI Talent Solution on AWS.

This first-of-its-kind platform is interoperable with clients’ human resources and workforce management systems. Skills.AI work products are also intended to corral labor departments, workforce development board community partners, NGOs, vendors, and businesses alike into clean job supporting coalitions.

The platform is operational proof that correctly modelled AI can save clients time and expense in HR, while fueling stakeholders’ push towards equitable clean energy employment.

Conclusion

Accenture is excited to continue building and expanding partnerships with utilities clients and AWS to empower businesses with analytics solutions to advance their clean energy initiatives.

Accenture’s Applied Intelligence data-led transformation (DLT) suite of capabilities can effectively support an organization’s strategic and technical transformations, no matter where the business sits on the analytics maturity spectrum. Accenture can with your business and IT leadership to take strong next steps towards building a clean energy future.

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