Customized data analytics process for Power Delivery and Utilization (PDU) organizations

Unique to Electric Distribution & Utilization

Ingest-to-store

• Decisions that consumers make have a huge impact in aggregate
• The network of intelligent devices is growing with different protocols and data formats, where the same event is measured in different ways
• At ingestion, it is critical not to throw out any data

Cleanse & Data Prep

• Data cleanse and prep takes a huge investment in time to curate and normalize. Expect information gaps in the data records
• Time series is not a snapshot in time. It changes by day, week, month, and year
• Need to determine how sensors are collecting information: constant monitoring or event based monitoring
• Given the massive amounts of data, start with a portfolio of prototype projects that can be scaled up

Visualize-to-Analyze

• Visualize the data to make it easier to see patterns, identify important variables and develop a plan for modeling
• Select manageable analytic data sets to use for data modeling
• Data at different resolutions and sampling may be a useful approach when the data is sparse
• Build several analytic models to help characterize and understand end-use load profiles and extract insights

Find more information here.