I would like to calculate a value based on the number of lines on outage at a given time.
An outage consists of the two endpoints and the voltage of the line:
outage = ["node1", "node2", "120"]
The historical values I have are based on per hour time stamps, so the format I believe I will need to feed the model is like so:
[timestamp, [outage1, outage2, etc...], value]
Do most machine learning libraries accept data in a variable width format like this? Alternatively, the only other thing I can think of is to have a super-wide data set and do a one hot encoding where each column is a node and its value is the voltage (scaled perhaps).
Columns: [timestamp, node1, node2, node3, etc..., value] Values: ["1/1/2019 14:00", 120, 120, 0, etc..., 5]
There are thousands of nodes on this grid. What is the best approach for formatting this data in a way that most models will accept?