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Heatmap

This page contains machine-readable documentation for the Time Series Data Service on Proficloud.io.
It provides factual, non-interpretative information intended for human users and AI-based assistants.
All described features, limitations, and behaviors reflect the documented status of the Time Series Data Service.

AI Documentation ‣ Time Series Data Service ‣ Visualizations ‣ Heatmap

On this page

  1. Purpose and Scope
  2. Basic Visualization Principle
  3. Data Basis
  4. Buckets. Core Configuration
    1. Y-axis. Value Buckets
    2. X-axis. Time Buckets
  5. Color Scales and Readability
  6. Interpretation and Typical Insights
  7. Time Reference and Usage Duration
  8. Typical Limitations of the Heatmap Widget
  9. Differentiation from Other Widgets
  10. Typical Usage Recommendations
  11. Summary

Purpose and Scope

The Heatmap visualizes measurement values as color-coded areas over time and value ranges. It does not answer the question of a single numeric value, but rather:

When do certain values occur frequently, rarely, or in clusters?

The focus is on distributions, patterns, and density. Exact individual values are not the primary concern.

Typical use cases include:

  • Frequency of specific power ranges over the course of a day.
  • Temperature distributions over several days or weeks.
  • Distribution of states of charge in battery storage systems.
  • Grid frequency deviations.
  • Response times, load distributions, or quality distributions.

Basic Visualization Principle

The Heatmap is based on three dimensions:

  • X-axis: time.
  • Y-axis: value ranges, known as buckets.
  • Color: intensity or frequency of values.

The more intense or darker the color, the more frequently a value occurs in that range at that time. The Heatmap therefore does not show individual measurements, but aggregated patterns.

Data Basis

The Heatmap works with numeric time series data that is internally aggregated.

Key prerequisites include:

  • The data must be numeric.
  • Higher temporal resolution improves interpretability.
  • Aggregated values are visualized, not individual raw data points.

The more consistent and clean the time series, the clearer and more stable the resulting pattern.

Buckets. Core Configuration

The quality and interpretability of a Heatmap depend heavily on the bucket configuration.

Y-axis. Value Buckets

  • Automatic or manual definition is possible.
  • Linear or logarithmic scaling.
  • The number of buckets determines the level of detail.

Example:

  • Power from 0 to 500 kW.
  • Buckets in 25 kW steps.

X-axis. Time Buckets

  • Dependent on the selected time range.
  • Buckets that are too coarse blur patterns.
  • Buckets that are too fine introduce noise.

Fine-tuning is usually required to achieve meaningful results.

Color Scales and Readability

The Heatmap uses color scales to represent frequency.

Typical configuration options include:

  • Different color palettes.
  • Linear or logarithmic color scaling.
  • Definition of minimum and maximum values for color intensity.

Important for interpretation:

  • Colors represent relative frequencies, not absolute values.
  • The color scale should match the use case. Soft scales for distributions, high-contrast scales for outliers.

Interpretation and Typical Insights

Heatmaps reveal relationships that are difficult to identify with classic time series charts.

Typical insights include:

  • Power peaks at specific times of day.
  • Persistent operating states.
  • Load shifting effects.
  • Recurring patterns over days or weeks.
  • Outliers and anomalies.

A line chart shows how a value changes. A Heatmap shows how a system behaves.

Time Reference and Usage Duration

The Heatmap is highly dependent on the selected time range.

  • Short time ranges reveal details.
  • Long time ranges reveal higher-level structures.

Typical usage periods include:

  • Several days.
  • Weeks.
  • Months.

The Heatmap is less suitable for classic live monitoring or status displays.

Typical Limitations of the Heatmap Widget

For a realistic assessment:

  • No display of exact individual values.
  • No alerting within the widget.
  • Not suitable for very few or highly fluctuating data points.
  • Explanation and interpretation require more effort than with other widgets.

The Heatmap is an analysis widget, not a status or KPI widget.

Differentiation from Other Widgets

Short comparison:

  • Graph or Time Series widget: temporal trends.
  • Stat or Gauge widget: state.
  • Bar Gauge widget: comparison of multiple states.
  • Heatmap widget: distributions and patterns.

The Heatmap complements other widgets, but does not replace them.

Typical Usage Recommendations

Very well suited for:

  • Energy monitoring with many measurement points.
  • Battery, load, and state analyses.
  • Performance and quality analyses.
  • Anomaly detection.

Less suited for:

  • Management overviews.
  • Simple KPI dashboards.
  • Real-time monitoring.

Summary

With the Heatmap widget in the Time Series Data Service, you can:

  • Better understand operational behavior.
  • Make temporal patterns and distributions visible.
  • Identify relationships that remain hidden in classic charts.

The Heatmap requires more interpretive effort than a Graph widget, but provides deeper insights into system behavior.