This Guidance helps utility companies ingest data from Meter Data Management Systems (MDMS) or directly from Head End Systems (HES) and combine them with other data sources, including weather and geographic information system (GIS) data. Utility companies will be able to detect meter and distribution circuit anomalies, run circuit balancing, thwart energy theft, predict demand, and enhance customer engagement with proactive analytics and artificial intelligence and machine learning (AI/ML)-based forecasts and predictions.
Please note: This Guidance has been updated. The Architecture Diagram is an enhanced version that automatically deploys the following new features: data lake, data ingestion/ML pipelines, visualization components, MDMS/HES simulator, and enhanced load testing. The sample code has also been updated with the new functionalities.
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Step 1
Everything you need to launch this Guidance in your account is right here.
A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment. The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
AWS Glue and Athena organize data and monitor data access. You can further analyze data with dashboards, reports, and notifications in QuickSight and Amazon Managed Grafana. You can access these dashboards from any device and embed them into your apps and websites. AWS CloudFormation manages the infrastructure and application stack, allowing you to make changes and test the Guidance for different use cases.
The AWS Glue Data Catalog has encryption enabled. All metadata that AWS Glue writes to Amazon S3 is encrypted. We recommend designing AWS Identity and Access Management (IAM) roles using the principle of least privilege, which means granting the minimal amount of access needed for the role to complete a specific action under specific conditions. This will give data access to only the necessary users and resources.
AWS Glue, Amazon S3, and Athena are all serverless and will scale data access performance as your data volume increases. AWS Glue provisions, configures, and scales the resources required to run your data integration jobs. Athena queries your data without you having to set up and manage any servers or data warehouses. Amazon SNS and Amazon SQS allow you to increase data ingestion without disruption.
You can use this Guidance with your own meter read and adopt it to your requirements. Once your data is transformed to the internal data format (as detailed in the associated Implementation Guide), demo features and the dashboard work transparently.
This Guidance uses serverless services where possible, such as Lambda, Athena, and Kinesis, to help you avoid upfront costs and pay only for the resources you use. We use Amazon EMR in a non-serverless manner to control and optimize the nodes, making it more cost-effective based on your use case.
S3 Glacier archives data using magnetic storage rather than solid state memory, helping to optimize your storage footprint. Amazon SNS and Amazon SQS decouple applications to avoid having running resources waiting for load.