def setUpClass(cls):
ds = NREL()
cls.turbine = ds.get_turbine(NREL.park_id['tehachapi'], 2004, 2005)
cls.windpark = ds.get_windpark(NREL.park_id['tehachapi'], 3, 2004,
2005)
cls.pmapping = PowerMapping()
cls.pdmapping = PowerDiffMapping()
def amount_of_windmills(radius, park):
target = NREL.park_id[park]
ds = NREL()
windpark = ds.get_windpark(target, radius, 2004, 2005)
target = ds.get_windmill(target, 2004, 2005)
windmills = windpark.get_windmills()
return len(windmills)
This examples shows a statistical overview of the power levels of a wind turbine.
"""
# Author: Oliver Kramer <*****@*****.**>
# License: BSD 3 clause
import matplotlib.pyplot as plt
import numpy as np
import windml.util.features
from windml.datasets.nrel import NREL
from windml.visualization.show_coord_topo import show_coord_topo
ds = NREL()
windpark = ds.get_windpark(NREL.park_id['tehachapi'], 2, 2004)
X = np.array(windpark.get_powermatrix())
feat, month_power, ramps_up, ramps_down, power_freq =\
windml.util.features.compute_highlevel_features(windpark.turbines[0])
help = [
i * windml.util.features.interval_width
for i in range(1, 30 / windml.util.features.interval_width + 1)
]
labels = [
str(i - windml.util.features.interval_width) + "-" + str(i) for i in help
]
with plt.style.context("fivethirtyeight"):
figure = plt.figure(figsize=(8, 5))
# Author: Oliver Kramer <*****@*****.**>
# License: BSD 3 clause
from __future__ import print_function
import sklearn
import numpy as np
import pylab as plt
from sklearn import manifold, decomposition
from builtins import range
from windml.datasets.nrel import NREL
# load data and define parameters / training and test sequences
K = 30
ds = NREL()
windpark = ds.get_windpark(NREL.park_id['tehachapi'], 10, 2004)
X = np.array(windpark.get_powermatrix())
X_train = X[:2000]
X_test = X[2000:2000 + 200 * 4]
# computation of ISOMAP projection
print("computation of ISOMAP projection")
X_latent = manifold.Isomap(K, n_components=2).fit_transform(X_train)
# computation of sequence of closest embedded patterns
sequence = []
for x in X_test:
win = 0
smallest = 10E100
def test_get_windpark(self):
ds = NREL()
windpark = ds.get_windpark(NREL.park_id['tehachapi'], 10, 2004, 2005)
assert(len(windpark.mills) == 66)
def test_get_windpark(self):
ds = NREL()
windpark = ds.get_windpark(NREL.park_id['tehachapi'], 10, 2004, 2005)
assert (len(windpark.turbines) == 66)
def setUpClass(cls):
ds = NREL()
cls.turbine = ds.get_turbine(NREL.park_id['tehachapi'], 2004)
cls.windpark = ds.get_windpark(NREL.park_id['tehachapi'], 3, 2004)
def setUpClass(cls):
ds = NREL()
cls.windmill = ds.get_windmill(NREL.park_id['tehachapi'], 2004, 2005)
cls.windpark = ds.get_windpark(NREL.park_id['tehachapi'], 3, 2004, 2005)
cls.pmapping = PowerMapping()
cls.pdmapping = PowerDiffMapping()
def setUpClass(cls):
ds = NREL()
cls.windmill = ds.get_windmill(NREL.park_id['tehachapi'], 2004)
cls.windpark = ds.get_windpark(NREL.park_id['tehachapi'], 3, 2004)
def setUpClass(cls):
ds = NREL()
cls.turbine = ds.get_turbine(NREL.park_id['tehachapi'], 2004)
cls.windpark = ds.get_windpark(NREL.park_id['tehachapi'], 3, 2004)
""" Flip-Book of Wind Speed ------------------------------------------------------------------------- This example illustrates the wind speed of the turbines in the park 'tehachapi'. The figures visualize the wind situation at four different times (with a difference of 20 min). The turbines are colorized with regard to the wind strengths (from strong in red to low in blue). """ # Author: Nils A. Treiber <*****@*****.**> # License: BSD 3 clause from windml.datasets.nrel import NREL from windml.visualization.show_flip_book import show_flip_book ds = NREL() windpark = ds.get_windpark(NREL.park_id["tehachapi"], 30, 2004) show_flip_book(windpark, 4, 3460, 2)
