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NB: this ticket is work in progress. I don't agree with my own analysis yet, but I couldn't afford to linger on this. In general we don't support scheduling under heterogeneous sensor resolutions, and there may be several issues at play here, like:
Arrival times falling between interval boundaries of the power sensor.
Missing resampling of prices to the resolution of the power sensor.
What is the problem?
Scheduling an asset with a sensor resolution of 5 minutes against market prices with a resolution of 15 minutes crashes the scheduler.
Analysis
The length of the price data is less than the length of the power schedule.
Speculation
The time range for the price query doesn't return all the prices that are needed. For example: scheduling per 5 minutes from 13:50 to 16:05 right now obtains prices per 15 minutes from 14:00 to 16:00. After resampling, len(prices) == 24, but len(schedule) == 27.
Suggested fix for speculated problem
The query should be filtered using start and end differently. Right now we have:
NB: this ticket is work in progress. I don't agree with my own analysis yet, but I couldn't afford to linger on this. In general we don't support scheduling under heterogeneous sensor resolutions, and there may be several issues at play here, like:
What is the problem?
Scheduling an asset with a sensor resolution of 5 minutes against market prices with a resolution of 15 minutes crashes the scheduler.
Analysis
The length of the price data is less than the length of the power schedule.
Speculation
The time range for the price query doesn't return all the prices that are needed. For example: scheduling per 5 minutes from 13:50 to 16:05 right now obtains prices per 15 minutes from 14:00 to 16:00. After resampling,
len(prices) == 24
, butlen(schedule) == 27
.Suggested fix for speculated problem
The query should be filtered using start and end differently. Right now we have:
Actually this seems correct already.
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