Waterfall Widget

Introduction

Description

The Waterfall Widget is a specialized bar chart used to visualize the cumulative impact of sequential data points. Each bar represents a component of a total value, either derived from aggregated time-series data or computed from other bars, allowing users to break down complex metrics into understandable segments. The widget supports grouping related bars under common categories and visually highlighting them with colored zones and labels.

Purpose

This widget helps renewable energy professionals analyze the contribution of different factors to a total value over time. It is especially useful for breaking down production losses, comparing budgeted vs. actual performance, and identifying key drivers of variation. By structuring data into a visual flow, users can pinpoint areas for improvement and understand the impact of uncontrollable vs. design-driven factors.

Target Audience

• Business Users: Asset managers, financial analysts, and operations leads seeking to understand performance gaps and loss attribution.

• Technical Users: Engineers and dashboard/report designers configuring visual breakdowns of production, efficiency, or financial metrics.

Key Features & Use Cases

1. Sequential Breakdown of Aggregated Metrics

Create each bar using aggregated time-series data or computed values to show how individual components contribute to a total.

Use Case – Solar Production Comparison
Compare P50 Budgeted Production against P50 Actual Production, with intermediate bars showing losses due to temperature, shading, grid curtailment, and system faults. This helps stakeholders understand the gap between expected and actual output.

2. Grouping Bars by Category

Group related bars under a common category and visually highlight them with a colored zone and a title.

Use Case – Wind Farm Loss Attribution
Losses are grouped into:

• Environmental Losses: Wind variability, icing  

• Forced Losses: Grid curtailment, mechanical faults
  This grouping helps asset managers quickly identify which category contributes most to the production gap.

3. Labeling for Improvement Potential

Label bar groups to distinguish between design-driven, uncontrollable, and improvable factors.

Use Case – BESS Performance Review
Break down round-trip efficiency losses into:

• Design Limits (e.g., inverter efficiency)  

• Uncontrollable Factors (e.g., ambient temperature)  

• Improvement Opportunities (e.g., operational strategy, maintenance delays)
  This helps technical teams prioritize actionable improvements.

Data Types Used

The Waterfall Widget supports a range of renewable energy data types, typically aggregated or computed over time:

• Energy Production Metrics – Budgeted vs. actual output, energy losses

• Loss Attribution Data – Environmental, forced, and system-related losses

• Efficiency Metrics – Round-trip efficiency, performance ratio

• Financial Metrics – Revenue loss due to curtailment or faults

• Calculated Properties – Delta values, cumulative impacts, improvement potential

Each bar can be based on raw time-series data, aggregated values, or derived calculations from other bars.

Widget Use in BI App Types

Use in Dashboards

In Dashboard BI Apps, the Waterfall Widget provides interactive, visual breakdowns of performance metrics. It is ideal for real-time or recent historical analysis of production gaps, loss attribution, and efficiency trends.

Example – Solar Portfolio Dashboard
A dashboard displays a waterfall chart comparing expected vs. actual production for a solar site, with bars showing losses grouped by category. This helps operations teams and asset managers identify which factors are driving underperformance and where to focus mitigation efforts.

Use in Reports

In Report BI Apps, the Waterfall Widget supports structured, time-based summaries for performance reviews and financial reporting. It helps stakeholders understand the composition of key metrics and track changes over time.

Example – Quarterly Wind Farm Report
A report includes a waterfall chart showing the breakdown of budgeted vs. actual energy output, with labeled categories for environmental and forced losses. This supports executive-level review and strategic planning.