Artificial and Human Intelligence with Digital Twins
Figure 6: Solar Farm and Power Output Chart
In this case, there is a cyclical component
that could be forecasted using traditional
methods, but there is a less well modeled
component of weather and cloud cover.
With the large amount of data available from
the solar farm and nearby solar farms, a
deep learning RNN can capture the more
sporadic aspects of the energy output. 11
The process for training an RNN is different
if you are working with sequence data versus
working with temporal data. The process for
training the RNN with sequence data is as
follows:
intervals, and we use the previous
minute of data.
Create a target variable for the
events of interest and use it to label
the sequences where the event
occurs. For our example, we are
using motor starts and identifying
weak motor starts indicating
capacitor failure.
Train the RNN. Bidirectional model
fitting is not needed in this case
because measurement data is always
moving forward in time.
The trained model can then be deployed for
inferencing. In most cases, the model
inferencing function will be sufficiently fast
to be used on the real-time measurement
stream, either in the cloud, server or edge
device.
Break the data into segments of
sequential measurements. The
length of the segment is determined
by the time interval of the data and
the expected duration of the
precursor to an event. For the energy
circuit example in smart buildings,
the data is collected at 5-second
The second type of RNN is used to forecast.
The example in this case is to forecast the
energy output of a solar farm for short time
periods in the future (1 hour). The key in this
11
Kahler, Susan. 2018. “Using Deep Learning to Forecast Solar Energy.” Available:
https://blogs.sas.com/content/subconsciousmusings/2018/07/05/deep-learning-forecasts-solar-power/
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