def experiment(method, windpark, windpark_test, damaged, rate):
args, nseries = argfuncs[method](windpark)
reconstructed = interpolate(damaged, **args)
target = windpark.get_target()
measurements = repair_nrel(target.get_measurements()[:10000])
turbines = windpark.get_turbines()
for t in range(len(turbines)):
turbines[t].add_measurements(\
repair_nrel(turbines[t].get_measurements()[:10000]))
# this is the target turbine, use the reconstructed here.
turbines[-1].add_measurements(reconstructed)
feature_window, horizon = 3, 3
mapping = PowerMapping()
# with damaged
X = mapping.get_features_park(windpark, feature_window, horizon)
Y = mapping.get_labels_turbine(target, feature_window, horizon)
train_to = int(math.floor(len(X)))
train_step, test_step = 1, 1
reg = linear_model.LinearRegression()
reg = reg.fit(X[0:train_to:train_step], Y[0:train_to:train_step])
# USE THE 2005 YEAR FOR TESTING, WITHOUT DAMAGE
# predict on second year without damage
turbines = windpark_test.get_turbines()
for t in turbines:
t.add_measurements(repair_nrel(t.get_measurements()[:10000]))
target_test = windpark_test.get_target()
XT = mapping.get_features_park(windpark_test, feature_window, horizon)
test_to = int(math.floor(len(XT)))
YT = mapping.get_labels_turbine(target_test, feature_window,
horizon)[:test_to]
y_hat = reg.predict(XT[:test_to])
mse_y_hat = 0
for i in range(0, len(y_hat)):
y = YT[i]
mse_y_hat += (y_hat[i] - y)**2
mse_y_hat /= float(len(y_hat))
return mse_y_hat
def experiment(method, windpark, windpark_test, damaged, rate):
args, nseries = argfuncs[method](windpark)
reconstructed = interpolate(damaged, **args)
target = windpark.get_target()
measurements = repair_nrel(target.get_measurements()[:10000])
turbines = windpark.get_turbines()
for t in range(len(turbines)):
turbines[t].add_measurements(\
repair_nrel(turbines[t].get_measurements()[:10000]))
# this is the target turbine, use the reconstructed here.
turbines[-1].add_measurements(reconstructed)
feature_window, horizon = 3,3
mapping = PowerMapping()
# with damaged
X = mapping.get_features_park(windpark, feature_window, horizon)
Y = mapping.get_labels_turbine(target, feature_window, horizon)
train_to = int(math.floor(len(X)))
train_step, test_step = 1, 1
reg = linear_model.LinearRegression()
reg = reg.fit(X[0:train_to:train_step], Y[0:train_to:train_step])
# USE THE 2005 YEAR FOR TESTING, WITHOUT DAMAGE
# predict on second year without damage
turbines = windpark_test.get_turbines()
for t in turbines:
t.add_measurements(repair_nrel(t.get_measurements()[:10000]))
target_test = windpark_test.get_target()
XT = mapping.get_features_park(windpark_test, feature_window, horizon)
test_to = int(math.floor(len(XT)))
YT = mapping.get_labels_turbine(target_test, feature_window, horizon)[:test_to]
y_hat = reg.predict(XT[:test_to])
mse_y_hat = 0
for i in range(0, len(y_hat)):
y = YT[i]
mse_y_hat += (y_hat[i] - y) ** 2
mse_y_hat /= float(len(y_hat))
return mse_y_hat
def experiment(method, windpark, damaged, rate):
args = argfuncs[method](windpark)
reconstructed = interpolate(damaged, **args)
error, var, std = scores(measurements, reconstructed)
return error, var, std
from numpy import array, zeros, float32, int32
# get windpark and corresponding target. forecast is for the target turbine
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
measurements = target.get_measurements()[300:1000]
damaged, indices = destroy(measurements, method="nmar", percentage=.80,\
min_length=10, max_length=100)
neighbors = windpark.get_turbines()[:-1]
nseries = [t.get_measurements()[300:1000] for t in neighbors]
tinterpolated = interpolate(damaged, method='mreg',\
timestep=600,\
neighbor_series = nseries,\
reg = 'linear_model')
d = array([m[0] for m in tinterpolated])
y1 = array([m[1] for m in tinterpolated]) #score
y2 = array([m[2] for m in tinterpolated]) #speed
d_hat = array([m[0] for m in damaged])
y1_hat = array([m[1] for m in damaged])
y2_hat = array([m[2] for m in damaged])
d_true = array([m[0] for m in measurements])
y1_true = array([m[1] for m in measurements])
y2_true = array([m[2] for m in measurements])
d_time = []
gamma_range = [0.0001, 0.000001]
C_range = [2 ** i for i in range(-3, 5, 1)]
regargs = {
"epsilon" : 0.1,
"cv_method" : "kfold",
"cv_args" : {"k_folds" : 10},
"kernel" : 'rbf',
"tuned_parameters" : [{
'kernel': ['rbf'],
'C': C_range,
'gamma': gamma_range}]}
tinterpolated = interpolate(damaged, method = 'mreg',\
timestep=600,\
neighbor_series = nseries,\
reg = 'svr',
regargs = regargs)
d = array([m[0] for m in tinterpolated])
y1 = array([m[1] for m in tinterpolated]) #score
y2 = array([m[2] for m in tinterpolated]) #speed
d_hat = array([m[0] for m in damaged])
y1_hat = array([m[1] for m in damaged])
y2_hat = array([m[2] for m in damaged])
d_true = array([m[0] for m in measurements])
y1_true = array([m[1] for m in measurements])
y2_true = array([m[2] for m in measurements])
"epsilon": 0.1,
"cv_method": "kfold",
"cv_args": {
"k_folds": 10
},
"kernel": 'rbf',
"tuned_parameters": [{
'kernel': ['rbf'],
'C': C_range,
'gamma': gamma_range
}]
}
tinterpolated = interpolate(damaged, method = 'mreg',\
timestep=600,\
neighbor_series = nseries,\
reg = 'svr',
regargs = regargs)
d = array([m[0] for m in tinterpolated])
y1 = array([m[1] for m in tinterpolated]) #score
y2 = array([m[2] for m in tinterpolated]) #speed
d_hat = array([m[0] for m in damaged])
y1_hat = array([m[1] for m in damaged])
y2_hat = array([m[2] for m in damaged])
d_true = array([m[0] for m in measurements])
y1_true = array([m[1] for m in measurements])
y2_true = array([m[2] for m in measurements])
from numpy import array, zeros, float32, int32
# get windpark and corresponding target. forecast is for the target turbine
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
measurements = target.get_measurements()[300:1000]
damaged, indices = destroy(measurements, method="nmar", percentage=.80,\
min_length=10, max_length=100)
neighbors = windpark.get_turbines()[:-1]
nseries = [t.get_measurements()[300:1000] for t in neighbors]
tinterpolated = interpolate(damaged, method='mreg',\
timestep=600,\
neighbor_series = nseries,\
reg = 'linear_model')
d = array([m[0] for m in tinterpolated])
y1 = array([m[1] for m in tinterpolated]) #score
y2 = array([m[2] for m in tinterpolated]) #speed
d_hat = array([m[0] for m in damaged])
y1_hat = array([m[1] for m in damaged])
y2_hat = array([m[2] for m in damaged])
d_true = array([m[0] for m in measurements])
y1_true = array([m[1] for m in measurements])
y2_true = array([m[2] for m in measurements])
d_time = []
# get windpark and corresponding target. forecast is for the target turbine
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
measurements = target.get_measurements()[300:1000]
damaged, indices = destroy(measurements, method="nmar", percentage=.80,\
min_length=10, max_length=100)
neighbors = windpark.get_turbines()[:-1]
nseries = [t.get_measurements()[300:1000] for t in neighbors]
tinterpolated = interpolate(damaged, method='mreg',\
timestep=600,\
neighbor_series = nseries,\
reg = 'knn',
regargs = {'n': 10, 'variant':'uniform'})
d = array([m[0] for m in tinterpolated])
y1 = array([m[1] for m in tinterpolated]) #score
y2 = array([m[2] for m in tinterpolated]) #speed
d_hat = array([m[0] for m in damaged])
y1_hat = array([m[1] for m in damaged])
y2_hat = array([m[2] for m in damaged])
d_true = array([m[0] for m in measurements])
y1_true = array([m[1] for m in measurements])
y2_true = array([m[2] for m in measurements])
