Crafting a Predictive Ramping Trend in PI AF: A Step-by-Step Guide

Crafting a Predictive Ramping Trend in PI AF: A Step-by-Step Guide

In today's industrial landscape, predictive analytics is not just a buzzword but a necessity. In this blog post, we'll delve into creating a predictive ramping trend in PI AF based on future tag data. With advancements in OSIsoft's PI System capabilities, managing and leveraging future data has become increasingly critical. Here’s how you can implement a robust predictive analysis in PI AF, tailored to anticipate changes based on future data inputs.

Understanding the Scenario

The task is to produce a ramping trend using data forecasted 7 hours into the future. The input future tag updates every hour on the dot (e.g., 12:00, 13:00). The goal here is to transition smoothly from the future tag readings into a predictive ramping trend, adjusting the values at 5 minutes before and after each hour to enhance forecasting accuracy.

The above visual illustrates the desired red line (the ramping trend), sourced from the future tag (represented by the blue line).

Why Future Data?

Future data, especially in dynamic industrial environments, allows proactive adjustments. This capability is crucial in sectors like energy management where anticipatory actions can lead to cost savings and increased operational efficiency.

Setting Up in PI AF

To achieve this, we must consider both the prediction model's trigger and the volatile nature of future predictions:

1. Inputs and Outputs: Identify the inputs (future tag) and configure outputs using PI AF attributes. Ensure they are correctly structured to handle override of future outputs based on recalculations if source data changes.

2. PI AF Analysis: Configure an analysis that effectively writes future tag data based on an hourly schedule, incorporating a timestamp override. Use PI’s analytics capabilities to interpolate and adjust the values at hh:05 and hh:55 timestamps.

3. Periodic Trigger Adjustments: With PI's default write mode set to ArcReplace, outputs can automatically update any predictive changes. By using periodic triggers, analyses are refreshed, recalculating trends as new future data is available.

4. System Triggers and Scheduling: While direct event-driven triggers can be limited by future timestamps, configuring periodic triggers every hour ensures continuous analytics recalibration.

5. Error Handling and Data Quality Check: An often overlooked yet important aspect is ensuring data integrity and handling errors, maintaining system reliability even in a predictive environment.

6. Future and Historical Analyses Coordination: Balance predictive analytics by understanding how future data transitions into historical records, ensuring smooth operational transitions.

Implementing and Testing

Implement analysis testing with a loop setup, possibly integrating initial analysis results into SQL or using AFTable configurations to manage a cascading series of refined data based on the initial predictions.

"Iterate frequently to validate the ramp against actual outcomes," suggests Christer Lindquist, who underscores the value of adjusting real-time based on refined model feedback.

Conclusion

Using PI AF for predictive analytics, especially in handling future data, elevates operational foresight and efficiency. Applying smart data-driven decision frameworks like these makes it possible to convert complex data streams into actionable insights, optimizing outcomes in real-time.

Explore these implementation techniques to maximize your PI System investments, and keep innovating with OSIsoft’s evolving tools.