in this video we are going to create a Power BI report
with the data published by the PI Integrator. Let's get started.
To start creating the actual report click the report button at the left side of the window.
Right click the page tab to rename the page to wind generation overview.
We will add more pages to the report later on and by naming them we will ensure easy navigation.
Let's start putting content to the report by adding a title.
From the home tab of the ribbon click text box. Set the font size in the second drop down menu
from the left to 36. Type wind generation overview and adjust the box size by dragging a corner.
Now we will create a funnel chart to show energy by wind farm. So let's click on the
funnel chart under visualizations on the right side of the window to add a funnel chart to the
report. For group add the wind farm from turbine information by dragging and dropping.
For the values add the energy field from turbine data one hour summary.
Now we will create gauges to show availability and capacity factor. Remember to always click into
blank space before creating a new visualization. This is to ensure that you don't modify
existing visualizations by mistake. So let's do it and then click on the gauge icon under
visualizations. For value choose availability from the turbine data one hour summary.
You can notice that the value doesn't match what should be expected.
The reason for this is that Power BI generally defaults to sum as the default summarization,
but availability is more useful as an average. To change this behavior let's click on availability
in the field section on the right and then in the ribbon navigate to column tools.
In the center change the summarization to average and set the format to percentage.
By publishing the data as a ratio Power BI can convert the data to a percentage. This helps
Power BI choose default ranges for gauges and add the percent symbol to the values. Notice however
that the data in the gauge has not changed but the title has updated to sum of availability.
This is because changing the default summarization in Power BI doesn't alter existing visualizations.
To do this let's click that drop down on the value and choose average.
You can see that the visual now shows an appropriate availability.
So let's review this process to add capacity factor to the report page.
Now we will add a trend showing power over time. So let's add a line chart to the report
for the axis. Choose timestamp from turbine data one hour summary,
in the drop down change the date hierarchy to timestamp.
For the values choose power from turbine data one hour summary.
Also add rated power from the turbine information.
Remember to ensure that power and rated power are aggregated as a sum. You can
check it by holding the mouse over the entries in the value category.
Now we will create a new measure for combined power.
In the previous visualization power was aggregated as a sum; however in most cases
data that contains rates such as flow rate speed or power should be averaged over time and not
summed. Let's create a table so that we can see it better. For value choose wind form
and rated power from turbine information and two times power from turbine data one hour summary.
Let's expand the drop down menu for the second power and choose average instead of sum.
Notice that when aggregated by wind farm the power as a sum has a widely inaccurate number.
This is because every row for the wind farm is being summed together.
Notice also that the average of power also isn't quite showing the expected value. Instead of
showing the average power for the wind farm it is showing the average power across all the turbines.
In the previous visual the power trend, the correct result was attained because the power
was summed up across each distinct timestamp and was not aggregated across timestamps.
We can obtain the same result by using the formula that you can see on the screen.
So let's recreate this in Power BI.
First click new measure from the home tab of the ribbon and then type in the formula.
In the measure tools tab ensure that the home table is set to turbine data one hour summary.
Adding this value to the table shows that the result matches now our expectations.
So now we will add a date slicer to allow date filtering in the report. Let's add a
slicer visualization and use the timestamp from turbine data one hour summary as the field.
When hovering on the slicer, use the drop down at the top right
to change the type of filtering applied. For this report choose relative date.
Set the slicer to show the last 7 days.
The report will now update to show the selected time range.
This concludes the video on creating the Power BI report. Thanks for watching.