Peak Load Detection & Seasonal Demand Analysis for District Heating Optimization

The Challenge

District heating operators observed short-term drops in outside temperature that triggered sudden peak mass-flow loads from the power plant. These peaks were unnecessary and inefficient because by the time the heat reached customers, ambient temperature had already risen again.

Without structured analysis, the team could not determine:

• When peak loads occurred most frequently during the year
• Whether peaks were driven by real demand or transient temperature fluctuations
• How often resources were over-allocated due to short-term weather effects
• How to proactively detect and prevent inefficient load spikes

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The Approach

The team built a structured analytical workflow in TrendMiner to detect, contextualize, and monitor peak loads.

• Delta aggregations were created for outside temperature and mass flow
• A formula was developed to time-shift mass flow signals for alignment analysis
• Value-Based Searches identified peak load events
• Search results were saved as context items for traceability
• Monitors were configured to automatically detect future peaks and generate context events
• A dashboard was built to visualize all relevant information

For seasonal analysis:

• A global timeframe was defined in DashHub
• Monthly VBS calculations identified peak frequency patterns
• Nested logic formulas consolidated search conditions into a single detection signal

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The Results

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The Takeaway

By transforming short-term temperature fluctuations into structured operational insights, the team enabled proactive peak detection, improved energy dispatch efficiency, reduced unnecessary resource consumption, and established a scalable framework for load optimization across different operating profiles.

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