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      • Generating a Room Temperature Heatmap
      • Modeling Fridge Content Temperatures
      • Outlier Detection on Multiple Temperature Sensors
      • Simple Temperature Forecasting for Substation Transformers
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  • Generating a Room Temperature Heatmap
  • Modeling Fridge Content Temperatures
  • Outlier Detection on Multiple Temperature Datastreams
  • Simple Temperature Forecasting for Substation Transformers
  • Sensor Data Insight with Power BI Integrations
  • Third-Party Sensor Data in DT Cloud

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Application Notes

Index page for the various application notes written to experiment with different sensor use-cases.

Last updated 11 months ago

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Generating a Room Temperature Heatmap

Continuously logging temperature data in an office- or industry environment can provide a range of benefits. In this application note, a method of combining several Disruptive Technologies (DT) temperature- and door-proximity sensors to generate a temperature heatmap is proposed. Using a room layout representation, the heatmap gradients are calculated by inverse distance weighting. .

Modeling Fridge Content Temperatures

Outlier Detection on Multiple Temperature Datastreams

Simple Temperature Forecasting for Substation Transformers

Forecasting time series data can be useful in many applications, be it simply for acquiring an overview of expected behavior or as incorporated in more complex anomaly detection systems. In this application note, the popular Holt-Winters model has been implemented and used for decomposing and forecasting seasonal transformer temperature data. Read more.

Sensor Data Insight with Power BI Integrations

Third-Party Sensor Data in DT Cloud

Continuously logging temperature data can help ensure the quality of refrigerated items in cold storage. However, in applications where the fridge door is opened in rapid succession, the unit's ambient air temperature may not reflect that of the items stored there. In this application note, a method for indirectly monitoring the core temperatures of items through heat-transfer modeling is explored. .

When running large-scale services, continuously monitoring asset temperatures can provide essential information for smooth long-term operation. Due to their small size and long-lasting battery life, Disruptive Technologies (DT) Temperature Sensors are well suited for simultaneously monitoring many assets. In this application note, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is applied on a stream of 25 temperature sensors with the aim of catching outlier events. .

Disruptive Technologies (DT) sensors can be used to gather high-resolution environmental data which in turn can be used to develop models and aggregate analysis. In this application note, we will look at how the POWER BI platform can be used to develop an analytics solution on data continuously gathered using DT sensors and a Data Connector. .

While our current range of devices already covers many use cases, some sensor types, like barometric pressure, are yet not in our repertoire. In this application note, we will take a look at how a Raspberry Pi can be used to sample barometric pressure data from a Mikroe Environment Click breakout board, then periodically publish events to the DT Cloud using our Python Emulator API. .

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