Time series

This page shows how to load time series data. All examples below expects you to have an initialized and authenticated instance of the client called eq.

Operations described here are available under eq.timeseries.*.

Requirements: Use these operations for curves with curve_type set to any of the following:

  • TIMESERIES

  • SCENARIO_TIMESERIES

Load time series data

Loading time series data is quite straight-forward. You need to specify three parameters at minimum: curve, begin and end.

So, let’s load data for a curve called DE Wind Power Production MWh/h 15min Actual from 1 January 2020 at 00:00 (inclusive) to 6 January 2020 at 00:00 (exclusive).

In the example below we specified the curve as a string. You can either supply the curve name or you can supply a Curve object. The same goes for the date parameters, where the allowed types are: date, datetime or an ISO-8601-formatted string.

(Keep in mind that if you do not have a paid subscription on Energy Quantified, you will only be able to load data 30 days back from today. So in that case you should adjust the begin and end dates accordingly.)

>>> from datetime import date
>>> timeseries = eq.timeseries.load(
>>>    'DE Wind Power Production MWh/h 15min Actual',
>>>    begin=date(2020, 1, 1),   # or begin='2020-01-01'
>>>    end=date(2020, 1, 6)      # or end='2020-01-06'
>>> )

The response is an energyquantified.data.Timeseries instance:

>>> timeseries.curve
<Curve: "DE Wind Power Production MWh/h 15min Actual", curve_type=TIMESERIES>

>>> timeseries.resolution
<Resolution: frequency=PT15M, timezone=CET>

>>> timeseries.begin()
datetime.datetime(2020, 1, 1, 0, 0, tzinfo=<DstTzInfo 'CET' CET+1:00:00 STD>)

>>> timeseries.end()
datetime.datetime(2020, 1, 6, 0, 0, tzinfo=<DstTzInfo 'CET' CET+1:00:00 STD>)

>>> timeseries.data
[<Value: date=2020-01-01 00:00:00+01:00, value=6387>,
 <Value: date=2020-01-01 00:15:00+01:00, value=6383>,
 <Value: date=2020-01-01 00:30:00+01:00, value=6640>
 ...

Aggregation

Notice that the wind actual curve in the above examples is in a 15-minute resolution. Energy Quantified do not a copy of that curve in hourly, daily or any other resolution.

If you would like to get the data in, say, daily resolution, simply supply an extra argument, frequency, when loading the time series data:

>>> from energyquantified.time import Frequency
>>> timeseries = eq.timeseries.load(
>>>    'DE Wind Power Production MWh/h 15min Actual',
>>>    begin=date(2020, 1, 1),
>>>    end=date(2020, 1, 6),
>>>    frequency=Frequency.P1D  # daily resolution
>>> )

>>> timeseries.resolution
<Resolution: frequency=P1D, timezone=CET>

>>> timeseries.data
[<Value: date=2020-01-01 00:00:00+01:00, value=8928.95>,
 <Value: date=2020-01-02 00:00:00+01:00, value=16302.95>,
 <Value: date=2020-01-03 00:00:00+01:00, value=32063.55>,
 <Value: date=2020-01-04 00:00:00+01:00, value=33299.36>,
 <Value: date=2020-01-05 00:00:00+01:00, value=13151.01>]

You can also decide the aggregation method. Let’s load the maximum wind production per day:

>>> from energyquantified.time import Frequency
>>> from energyquantified.metadata import Aggregation
>>> timeseries = eq.timeseries.load(
>>>    'DE Wind Power Production MWh/h 15min Actual',
>>>    begin=date(2020, 1, 1),
>>>    end=date(2020, 1, 6),
>>>    frequency=Frequency.P1D,
>>>    aggregation=Aggregation.MAX  # Max value per day
>>> )

>>> timeseries.data
[<Value: date=2020-01-01 00:00:00+01:00, value=14038>,
 <Value: date=2020-01-02 00:00:00+01:00, value=24891>,
 <Value: date=2020-01-03 00:00:00+01:00, value=36153>,
 <Value: date=2020-01-04 00:00:00+01:00, value=40671>,
 <Value: date=2020-01-05 00:00:00+01:00, value=18274>]

There is also support for hourly filters, such as BASE and PEAK. This is how you can load the mean wind production during peak hours:

>>> from energyquantified.time import Frequency
>>> from energyquantified.metadata import Aggregation, Filter
>>> timeseries = eq.timeseries.load(
>>>    'DE Wind Power Production MWh/h 15min Actual',
>>>    begin=date(2020, 1, 1),
>>>    end=date(2020, 1, 6),
>>>    frequency=Frequency.P1D,
>>>    aggregation=Aggregation.AVERAGE,
>>>    hour_filter=Filter.PEAK
>>> )

>>> timeseries.data
[<Value: date=2020-01-01 00:00:00+01:00, value=8578.48>,
 <Value: date=2020-01-02 00:00:00+01:00, value=16344.17>,
 <Value: date=2020-01-03 00:00:00+01:00, value=33363.6>,
 <Value: date=2020-01-04 00:00:00+01:00, value=37637.12>,
 <Value: date=2020-01-05 00:00:00+01:00, value=11912.42>]

When you specify a weekly, monthly, quarterly or yearly frequency, the API will automatically use futures peak (8-20 on workdays only) in the aggregation.

Load time series scenarios

Energy Quantified provides climate data, where we run the weather data for different years through our models (as of this writing, the weather years 1980-2019). Using the same method as above, eq.timeseries.load(), we can load this data.

For scenario-based time series, the values in timeseries.data[] are slightly different: It will consist of ScenarioValue items instead of Value items.

These ScenarioValue items contain a scenarios attribute instead of a value. The scenarios attribute is a tuple of the scenario values:

>>> from energyquantified.time import Frequency
>>> timeseries = eq.timeseries.load(
>>>    'DE Wind Power Production MWh/h 15min Climate',
>>>    begin=date(2020, 1, 1),
>>>    end=date(2020, 1, 6),
>>>    frequency=Frequency.P1D,
>>> )

>>> timeseries.data
[<ScenariosValue: date=2020-01-01 00:00:00+01:00, scenarios=(18988.74, 41907.79, 7712.76, 21450.4, 41017.22, 22006.53, 12535.5, 21720.46, 29565.86, 6424.07, 1977.56, 28206.2, 29880.71, 7876.56, 19262.9, 33366.47, 15903.28, 8025.6, 14447.35, 11107.51, 12495.92, 29776.22, 27195.17, 16943.26, 12084.37, 19026.09, 11743.87, 39982.1, 4164.34, 4904.58, 11775.45, 27830.02, 26543.89, 27228.76, 23010.97, 25048.93, 8048.41, 20949.78, 32833.12, 36763.43)>,
 <ScenariosValue: date=2020-01-02 00:00:00+01:00, scenarios=(14084.11, 36558.41, 12050.44, 23045.63, 37403.62, 16366.81, 20389.57, 27540.21, 43248.82, 2857.44, 1323.8, 40489.66, 37816.43, 14020.06, 24317.02, 29949.58, 8307.4, 8963.91, 31400.21, 22819.79, 15685.59, 26084.74, 20688.21, 23337.25, 12612.22, 40286.53, 3514.48, 30465.93, 15903.16, 4044.47, 7726.84, 18038.68, 26574.65, 25633, 29554.52, 40121.31, 25454.32, 18422.81, 21586.78, 30514.11)>,
 ...