I've bee reading through several of the examples and had a question regarding
handling multiple streams of data. I see in the the onegym swarm_description.py
a description of the included fields as follows:
"includedFields": [
{
"fieldName": "timestamp",
"fieldType": "datetime"
},
{
"fieldName": "kw_energy_consumption",
"fieldType": "float",
"maxValue": 53.0,
"minValue": 0.0
}
],
So in the onegym example, there appears to be one stream of data which has a
timestamp and the energy consumed.
I'm wondering how to handle a case where there are many streams such that there
is a timestamp and energy consumed for many meters. For example
[{household: "household-1", meter: "energy", timestamp: timestamp,
kw_energy_consumption: 0.0},
{household: "household-2", meter: "energy", timestamp: timestamp,
kw_energy_consumption: 0.0},
…]
In that example for each household there is a energy meter which has a
timestamp and kw_energy_consumption. Extending on the first example
[{household: "household-1", meter: "energy", timestamp: timestamp,
kw_energy_consumption: 1000.0},
{household: "household-1", meter: "temperature", timestamp: timestamp,
degrees_f: 65.0},
{household: "household-2", meter: "energy", timestamp: timestamp,
kw_energy_consumption: 1500.0},
{household: "household-2", meter: "temperature", timestamp: timestamp,
degrees_f: 72.0}
…]
there are now two meters per household. One for energy and one for temperature.
I was wondering what is the preferred way to specify the included fields.
As the final result what I would like to do is predict and do anomaly detection
on kw_energy_consumption and degrees_f per household. Is there a demo or
somewhere in the documentation where this is discussed?
Regards --Roland
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