Troubleshoot

In this article:

Gap in CUSUM view

A gap in the CUSUM is caused by a) missing Expected Energy, or b) missing Actual Energy.

Expected data missing

Expected energy is the output of the active energy model, and is used to calculate energy savings. Common reasons expected energy is missing is either it is purposely excluded due to a non-routine event or there’s missing data needed for the model calculation

1: In Performance, go to the Model tab click the model name

2: On the Output tab in the Graphs & Gaps section, click the Data Gaps tab. If the data gap is listed as a known exclusion, then no action is needed.

3: To further inspect missing data, go to the Data Streams tab under Performance to check Most Recent Data.

  • If you are responsible for uploading the data stream with the missing data, use the Add Data process to update your data. If data is automated, contact your Sensei representative.

When your data is up to date, you gain more value and greater ability to track and show savings.

CUSUM savings graph showing a gap in data

Add an event to the CUSUM graph as a reminder that this is a known, excluded day(s).

If the CUSUM and Actual Energy show the same gap, then it is typically missing energy data and may need to be marked as excluded day(s) by the Sensei representative.

Create an event

Actual energy data missing

Sensei obtains energy data in various ways. Often energy data arrives in an automated process, but some energy data streams are uploaded manually.

1: First, identify the Data Source for energy data on the Data Streams tab

2: If energy data is from Green Button, EPO, FTP, or a hardware device, notify your Sensei representative of missing data.

  • Alternatively, if energy data source shows as Manual, check the Most Recent Date. You may need to add data or contact your Sensei representative for missing data.

Unexpected data outliers

Hover over the outlier to view the data point value. If the outlier is due to:

1: A non-routine day resulting in higher or lower than normal data, then determine the reason for the outlier and add an event to describe the reason. If needed, take action to mitigate future outliers in consumption.

2: Erroneous data, check whether the data value is exactly twice as high as normal. It is possible that the same data was doubled for that timeframe. Inadvertent doubling of data is common. For other causes or erroneous data, you will need to research further. Contact your Sensei representative to help troubleshoot erroneous data.

CUSUM is flat

A flat or zero slope means the energy model is equal to actual consumption, resulting in no savings.

1: Review top priorities and compare consumption trends

2: Determine what action to pursue to save energy

Savings drops

Fluctuations in savings are expected. If savings drops for two consecutive months, then research the cause.