FAQ

Q: What is it?

A: This solution is for utility companies and other organizations that are looking to gain insights from smart-meter data. This data comes from meter data management (MDM) or similar systems. Insights include unusual energy usage, energy-usage forecasts, and meter-outage details.

Q: What are the user scenarios?

A: This solution aims to help customers in Power & Utilities industry who are seeking alternatives to MDMS (Meter Data Management Solution). Traditional MDMS systems have been available for over 15 years and most of them only provide the value proposition on billing. This Utility Meter Analytics Platform solution can help customers deep dive on value of meter data  and create more value-added scenarios.

Q: Who should use it?

A: Target users include:

1) Energy groups who provide power production, intelligent operation and comprehensive power management. They may seek for reliable solutions with capabilities of data collection, ingestion, transformation, analytic, prediction and persistent storage for their power generating equipment. They usually care about business flexibility and optimized cost to support their construction of distributed energy system.

2) Smart meter manufacturers who offer equipments connecting houses, buildings and factories directly and collect power usage data and operation data for power seller and property management companies. They may seek for reliable capabilities more than accurate billing to enhance product core value and market share.

Q: How does it work?

A: Data collected from edge, will be passed through ETL process (Extract, Transform, Load). The generated clean data will be further used for data query analysis and machine learning modeling. The solution provides functionalities for different customer use scenarios via RESTful API calls.

Q: What customer experience is like?

A: You can use this solution as your major IT/OT platform for creative application development and iteration.

Q: How much does it cost?

A: The solution includes Amazon Glue, Amazon SageMaker, Amazon RedShift, Amazon S3, Amazon Step Functions, Amazon Lambda, Amazon API Gateway, etc.

Among these services, major cost comes from Glue (for data ETL) and SageMaker (for model training deployment. According to standard pricing model:

  • Amazon Glue charges an hourly basis for the number of data processing units (DPU) (3.021 Yuan/DPU-Hour) in ETL tasks.
  • Amazon SageMaker charges based on running SageMaker instances (Instance type: m5.xlarge, 1.898 Yuan/Hour).

For actual cost in production environment, please consult with our sales team.

Q: Can I deploy the solution in any region?

A: No. This solution only supports for Amazon Web Services China (Beijing) Region and Amazon Web Services China (Ningxia) Region.

Q: What technical prerequisites do I need to prepare before starting?

A: It’s a one-click solution, you can directly deploy it into your account to do testing or demos.

To use it in production environment, you are expected to have basic Python skills (like Numpy/Pandas) in order to make actual raw data fit into Amazon Glue ETL process, have ability to integrate with RESTful APIs, and have Machine Learning knowledges to do hyperparameter tunning (DeepAR e.g.). If you don’t have the required technical abilities, you can always involve Amazon Web Services Partners or Amazon Web Services Professional teams to do the work for you. For more details, please contact Sales.

Q: What technical prerequisites do I need to prepare before starting?

A: The raw dataset and business data schema currently in this solution is a open-source dataset.

To use the solution in production system, you need to consider:

1) If your actual business schema can be reused (matched with the current default schema), you only need to modify the corresponding ETL jobs in Amazon Glue. If the actual business schema is different, you need to modify all ETL processes in Amazon Glue includes data partition and aggregation, as well as the API gateway back-end logic and SageMaker training process.

2) If your production system uses different data schema, then it is necessary to adapt data schema for ETL in Amazon Glue. It is recommended to keep fixed schema for back-end business data, making sure that the schema can be reused after different kinds of front-end data got processed via ELT. thereby minimizing the back-end integration work (such as making changes in API Gateway and SageMaker). The adaptation of different front-end data formats can be achieved through partners, joint development, etc. Please contact our sales team for more details.

3) The data format uploaded to S3 currently supports JSON, CSV, Parquet, ORC, Avro and Grok.

Q: What technical prerequisites do I need to prepare before starting?

A: It’s a one-click solution, you can directly deploy it into your account to do testing or demos.

To use it in production environment, you are expected to have basic Python skills (like Numpy/Pandas) in order to make actual raw data fit into Amazon Glue ETL process, have ability to integrate with RESTful APIs, and have Machine Learning knowledges to do hyperparameter tunning (DeepAR e.g.). If you don’t have the required technical abilities, you can always involve Amazon Web Services Partners or Amazon Web Services Professional teams to do the work for you. For more details, please consult with Amazon Web Services Sales team.

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