The PI Geek Talks are generally presented by partners and customers. The target audience is PI Admins and Developers. All the talks will be on Wednesday, Day 2, at the Parc 55 Hotel in the Powell room located on Level 3. You are invited to read the PI World Agenda for more information. Along with Day 2 Tech Talks, these are great reasons to take the walk from the Hilton to the Parc 55.
Selecting the Right Analytics Tool
David Soll, Omicron
There are several analytics tools and approaches available for working with PI data: Performance Equations, AF analytics, custom data references, PI ACE, PI DataLInk and Business Intelligence (BI) tools. It can be a quandary in determining which tool should you use for what. Should you focus on only one tool or use a mix? As it turns out, the answer is not as simple as basing it on the specific analytic. Other considerations should be put into the decision including: scalability, reliability, maintainability, and future-proofing, to name a few.
This talk will discuss the various tools available for performing analytics on PI data and their strengths and weaknesses, their scalability, reliability, maintainability, and future-proofing. The tools will be separated into two major classes: server side (persistent) analytics and client side (query time) analytics and the general differences between the two classes. Attendees will learn practical guidelines to for selecting analytics tools
Providing Enterprise Level Visibility for Snowflakes Using PI and AF
David Rodriguez, EDF Renewables, and Lonnie Bowling, Diemus
As part a larger project to monitor a large number of distributed wind farms throughout the US and Canada, the customer desired to have visibility into substation status information. This included showing substation one-line diagrams, voltage regulation status, breaker status, and events to notify them of any issues. Each wind project was design and installed by others which resulted in large differences between sites, include variability in networking, communications, and tag configuration. In other words, each project was like a snowflake. Using PI, AF Analytics, and Event frames, a solution was developed to normalized all wind projects. Once standardization was achieved, we then defined substation one-line circuits using an AF hierarchy. Data visualization was developed to provide on-demand, real-time rendering of circuits, voltage regulation trends, events, supporting information. This was implemented enterprise wide, and allowed for easy access and visibility for everyone in the organization.
Just Another Weather Application – Evaluating the OSIsoft Cloud System
Lonnie Bowling, Diemus
This session will showcase a weather application designed using the new OSIsoft Cloud System (OCS).
A backyard weather station was used as a data source for a live and historical data source. Forecasted data was then added to provide a complete picture of historical, current, and forecasted weather. Once all the data was streaming into an OCS sequencial data store, a full stack front-end solution was developed. This included an API layer in C#, Angular for the UI, and D3 for data visualization. A complete solution was developed to fully evaluate how OCS could be used in a real-life, purpose-built application. Key takeaways, including challenges, an architectural review, and source-code highlights will be shared.
Data Analytics to enhance Advanced Energy Communities planning and operation
John Rogers and Alberto Colombo, DERNetSoft
In today’s energy marketplace, poor energy awareness and a lack of data visibility coupled with the technical complexities of DER integration leads to a gap in local Advanced Energy Community development. DERNetSoft provides a scalable solution to this issue, making it possible to build advanced energy communities increasing energy awareness, enabling Distributed Energy Resources planning and supporting their operational optimization. We transform data into actionable insight and value-added advanced analytics and machine learning technique in the energy industry at the community level.
Data Quality & Shaping: Two Keys to Enabling Advanced Analytics & Data Science for the PI System
Camille Metzinger and Kleanthis Mazarakis, OSIsoft
Data quality is critical in the success of data-driven decisions. Issues with data will impact users across the organization- from operators, engineers, data scientists, to leaders. Answering business intelligence questions such as “which assets are performing well and which are under-performing” requires a birds-eye view of the data which may require (re)shaping of the data within the PI System. This talk and demo will explore the aspects of data quality and data shaping using PI System infrastructure by illustrating why they are so critical for success. We will also demonstrate the steps of how to improve Data Quality in the PI System and shape the PI System data to give it the right context for your advanced analytics.