def test_mreg_interpolation_multi(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:350]
damaged, indices = MARDestroyer().destroy(measurements, percentage=.50)
before_misses = MissingDataFinder().find(damaged, timestep)
neighbors = windpark.get_turbines()[:-1]
count_neighbors = len(neighbors)
reg = 'knn' # KNeighborsRegressor(10, 'uniform')
regargs = {'n' : 10, 'variant' : 'uniform'}
processed = 0
missed = {k : count_neighbors for k in indices}
exclude = []
damaged_nseries = []
for neighbor in neighbors:
nseries = neighbor.get_measurements()[300:350]
damaged, indices = MARDestroyer().destroy(nseries, percentage=.50, exclude=exclude)
for index in indices:
if(index not in missed.keys()):
missed[index] = count_neighbors
missed[index] -= 1
if(missed[index] == 1):
exclude.append(index) # exclude in next iterations
damaged_nseries.append(damaged)
t_hat = MRegInterpolation().interpolate(damaged, timestep=timestep,\
neighbor_series=damaged_nseries, reg=reg, regargs=regargs)
after_misses = MissingDataFinder().find(t_hat, timestep)
assert(len(after_misses) < 1)
def test_mreg_interpolation_multi(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:350]
damaged, indices = MARDestroyer().destroy(measurements, percentage=.50)
before_misses = MissingDataFinder().find(damaged, timestep)
neighbors = windpark.get_turbines()[:-1]
count_neighbors = len(neighbors)
reg = 'knn' # KNeighborsRegressor(10, 'uniform')
regargs = {'n' : 8, 'variant' : 'uniform'}
processed = 0
missed = {k : count_neighbors for k in indices}
exclude = []
damaged_nseries = []
for neighbor in neighbors:
nseries = neighbor.get_measurements()[300:350]
damaged, indices = MARDestroyer().destroy(nseries, percentage=.50, exclude=exclude)
for index in indices:
if(index not in missed.keys()):
missed[index] = count_neighbors
missed[index] -= 1
if(missed[index] == 1):
exclude.append(index) # exclude in next iterations
damaged_nseries.append(damaged)
t_hat = MRegInterpolation().interpolate(damaged, timestep=timestep,\
neighbor_series=damaged_nseries, reg=reg, regargs=regargs)
after_misses = MissingDataFinder().find(t_hat, timestep)
assert(len(after_misses) < 1)
def test_mreg_interpolation(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:500]
damaged, indices = MARDestroyer().destroy(measurements, percentage=.50)
before_misses = MissingDataFinder().find(damaged, timestep)
neighbors = windpark.get_turbines()[:-1]
reg = 'knn' # KNeighborsRegressor(10, 'uniform')
regargs = {'n' : 10, 'variant' : 'uniform'}
nseries = [t.get_measurements()[300:500] for t in neighbors]
t_hat = MRegInterpolation().interpolate(damaged, timestep=timestep,\
neighbor_series=nseries, reg=reg, regargs=regargs)
after_misses = MissingDataFinder().find(t_hat, timestep)
assert(len(after_misses) < 1)
def test_mreg_interpolation(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:500]
damaged, indices = MARDestroyer().destroy(measurements, percentage=.50)
before_misses = MissingDataFinder().find(damaged, timestep)
neighbors = windpark.get_turbines()[:-1]
reg = 'knn' # KNeighborsRegressor(10, 'uniform')
regargs = {'n' : 8, 'variant' : 'uniform'}
nseries = [t.get_measurements()[300:500] for t in neighbors]
t_hat = MRegInterpolation().interpolate(damaged, timestep=timestep,\
neighbor_series=nseries, reg=reg, regargs=regargs)
after_misses = MissingDataFinder().find(t_hat, timestep)
assert(len(after_misses) < 1)
def fun(citynum,methodnum,K):
park_id = NREL.park_id[cityname[citynum]]
windpark = NREL().get_windpark(park_id, 10, 2004,2006)
pla=[]
kk=windpark.get_turbines()
for i in range(len(kk)):
pla.append(kk[i].idx)
feature_window, horizon = 3, 3
mapping = PowerMapping()
data_1 = np.array(mapping.get_features_park(windpark, feature_window, horizon))
data_train = np.array(mapping.get_features_park(windpark, 1, 1))
lendata=len(data_1)
data1 = data_1[:lendata:3]
l1=len(data_train)
data_train1=data_train[:l1:3]
half=int(math.floor(len(data1) * 0.5))
traindata_1=data_train1[0:half,:]
traindata1=np.transpose(traindata_1)
traindata1=preprocessing.scale(np.array(traindata1),with_mean=True,with_std=True)
if methodnum==0:
ans = KMeans(n_clusters=K, random_state=0).fit(traindata1).predict(traindata1)
if methodnum==1:
ans = SpectralClustering(n_clusters=K, random_state=0).fit_predict(traindata1)
if methodnum==2:
ans = AgglomerativeClustering(n_clusters=K).fit_predict(traindata1)
if methodnum==3:
ans = Birch(n_clusters=K).fit_predict(traindata1)
if methodnum==4:
ans = DBSCAN(eps = 0.1).fit_predict(traindata1)
fo = open('cluster10/'+cityname[citynum]+method[methodnum]+str(K)+'.csv','w', newline='')
csv_write = csv.writer(fo,dialect='excel')
for i in range(len(ans)):
cc=[];
cc.append(pla[i])
cc.append(ans[i])
csv_write.writerow(cc)
fo.close()
def test_topological_interpolation(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 10, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:500]
damaged, indices = NMARDestroyer().destroy(measurements, percentage=.80,\
min_length=10, max_length=100)
tloc = (target.longitude, target.latitude)
neighbors = windpark.get_turbines()[:-1]
nseries = [t.get_measurements()[300:500] for t in neighbors]
nlocs = [(t.longitude, t.latitude) for t in neighbors]
t_hat = TopologicInterpolation().interpolate(\
damaged, method="topologic",\
timestep=timestep, location=tloc,\
neighbor_series = nseries,\
neighbor_locations = nlocs)
misses = MissingDataFinder().find(t_hat, timestep)
assert(measurements.shape[0] == t_hat.shape[0])
assert(len(misses) < 1)
def test_topological_interpolation(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 10, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:500]
damaged, indices = NMARDestroyer().destroy(measurements, percentage=.80,\
min_length=10, max_length=100)
tloc = (target.longitude, target.latitude)
neighbors = windpark.get_turbines()[:-1]
nseries = [t.get_measurements()[300:500] for t in neighbors]
nlocs = [(t.longitude, t.latitude) for t in neighbors]
t_hat = TopologicInterpolation().interpolate(\
damaged, method="topologic",\
timestep=timestep, location=tloc,\
neighbor_series = nseries,\
neighbor_locations = nlocs)
misses = MissingDataFinder().find(t_hat, timestep)
assert(measurements.shape[0] == t_hat.shape[0])
assert(len(misses) < 1)
import matplotlib.pyplot as plt
import matplotlib.dates as md
from pylab import *
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])
import matplotlib.pyplot as plt
import matplotlib.dates as md
from pylab import *
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, 5, 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]
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