Example

import reperiods as rp
from reperiods.datasets import load_renewable

# Loading a dataset of hourly capacity factor in a TemporalData object
data = load_renewable(24*7*2)
temporal_data = rp.TemporalData(data)
temporal_data.plot_curves()

Finding Representative Periods (RP)

Setting number of RP to select on the data and length in hours.

N_RP = 2
RP_length=48

Poncelet

# Using Poncelet method to find RP and their weights
temporal_data.calculate_RP(method = "poncelet", N_RP=N_RP, RP_length=RP_length, N_bins=15)
# Show RP selected (highlighted in green) and their weights
temporal_data.plot_RP()

K-medoids

# Using K-medoids method
temporal_data.calculate_RP(method = "kmedoids", N_RP=N_RP, RP_length=RP_length)
# Show RP selected (highlighted in green) and their weights
temporal_data.plot_RP()

Random

For comparison purpose, the random method randomly select RP and their weights.

# Using random method to find RP and their weights three times
temporal_data.calculate_RP(method = "random", N_RP=N_RP, RP_length=RP_length)
temporal_data.plot_RP()

Duration Curves

To evaluate RP and their weights it is possible to compare the duration curve approximation from RP and original data’s duration curve using plot_DC().

temporal_data.calculate_RP(method = "poncelet", N_RP=N_RP, RP_length=RP_length, N_bins=15)
temporal_data.plot_DC()
temporal_data.calculate_RP(method = "kmedoids", N_RP=N_RP, RP_length=RP_length)
temporal_data.plot_DC()
temporal_data.calculate_RP(method = "random", N_RP=N_RP, RP_length=RP_length)
temporal_data.plot_DC()
temporal_data.calculate_RP(method = "random", N_RP=N_RP, RP_length=RP_length)
temporal_data.plot_DC()
temporal_data.calculate_RP(method = "random", N_RP=N_RP, RP_length=RP_length)
temporal_data.plot_DC()

Poncelet

K-medoids

Random

Export

Export RP and their weights using export().

temporal_data.export("Folder_receiving_export")