Introduction

Description

The Analytic Map Widget is a geographic visualization tool designed to display spatial data using markers, overlays, and aggregated metrics. It enables users to monitor asset performance and health across multiple locations by layering historical data over time to reveal trends and patterns. This widget is ideal for visualizing KPIs such as availability, output, or fault frequency tied to specific geographic assets.

Purpose

The widget helps renewable energy operators and analysts gain spatial awareness of asset performance across distributed sites—such as solar farms, wind turbines, or battery storage facilities. By aggregating historical data and overlaying it on a map, users can identify regional trends, detect anomalies, and prioritize maintenance or optimization efforts.

Target Audience

Business Users: Asset managers, regional operations leads, and performance analysts seeking high-level geographic insights.
Technical Users: Engineers, data scientists, and system architects configuring spatial dashboards and reports for operational monitoring.

Key Features & Use Cases

1. Geographic Markers

Display assets as map markers with customizable icons and colors based on performance or health metrics.

Use Case – Wind Farm Overview
Markers represent individual turbines across multiple wind farms. Color coding indicates turbine health status (e.g., green for normal, red for fault), helping managers quickly identify problem areas.

2. Data Overlays

Overlay historical data such as energy output, fault frequency, or weather conditions to visualize trends over time.

Use Case – Solar Fleet Performance
An overlay shows average daily energy output across solar sites over the past month. Regions with lower output are flagged for further investigation.

3. Aggregated Metrics

Aggregate KPIs by location, region, or asset type to support comparative analysis.

Use Case – BESS Health Monitoring
Battery sites are grouped by region, with overlays showing average state-of-health and discharge efficiency. This helps prioritize maintenance in underperforming areas.

4. Interactive Filtering

Users can filter by asset type, time range, or performance thresholds to focus on specific data segments.

Use Case – Regional Fault Analysis
An operations lead filters the map to show only assets with fault rates above a defined threshold in the past 7 days, enabling targeted response planning.

Data Types Used

The Analytic Map Widget uses spatial and performance data structured as:

Location Coordinates – Latitude and longitude of each asset (e.g., turbine, solar array, battery site).
Time-Series Metrics – Historical data such as energy output, fault count, temperature, or availability.
Categorical Identifiers – Asset type, region, or site name for grouping and filtering.
Aggregated KPIs – Calculated metrics like average output, fault frequency, or health index over time.
Conditional Formatting Rules – Used to color markers or overlays based on performance thresholds.

Widget Use in BI App Types

Use in Dashboards

The Analytic Map Widget is available exclusively in Dashboards, where it provides real-time geographic monitoring of asset health and performance. It enables users to visualize spatial patterns, detect anomalies, and make informed operational decisions.

Example – Solar Operations Dashboard
A dashboard displays solar sites across a region with markers colored by daily output. Sites with declining performance are easily identified for follow-up, allowing asset managers to act quickly on underperformance.

Note: This widget is not available in Reports. For historical spatial analysis in reports, consider using tabular summaries or time-series visualizations grouped by region.

Analytic Map showing two solar farms in a region with simple performance overlay and color coding.

Analytics