def soiling_seperation_algorithm(observed, iterations=5, weights=None,
index_set=None, tau=0.85):
if weights is None:
weights = np.ones_like(observed)
if index_set is None:
index_set = ~np.isnan(observed)
zero_set = np.zeros(len(observed) - 1, dtype=np.bool)
eps = .01
n = len(observed)
s1 = cvx.Variable(n)
s2 = cvx.Variable(n)
s3 = cvx.Variable(n)
w = cvx.Parameter(n - 2, nonneg=True)
w.value = np.ones(len(observed) - 2)
for i in range(iterations):
# cvx.norm(cvx.multiply(s3, weights), p=2) \
cost = .1 * cvx.sum(tau * cvx.pos(s3) +(1 - tau) * cvx.neg(s3)) \
+ 10 * cvx.norm(cvx.diff(s2, k=2), p=2) \
+ .2 * cvx.norm(cvx.multiply(w, cvx.diff(s1, k=2)), p=1)
objective = cvx.Minimize(cost)
constraints = [
observed[index_set] == s1[index_set] + s2[index_set] + s3[index_set],
s2[365:] - s2[:-365] == 0,
cvx.sum(s2[:365]) == 0
# s1 <= 1
]
if np.sum(zero_set) > 0:
constraints.append(cvx.diff(s1, k=1)[zero_set] == 0)
problem = cvx.Problem(objective, constraints)
problem.solve(solver='MOSEK')
w.value = 1 / (eps + 1e2* np.abs(cvx.diff(s1, k=2).value)) # Reweight the L1 penalty
zero_set = np.abs(cvx.diff(s1, k=1).value) <= 5e-5 # Make nearly flat regions exactly flat (sparse 1st diff)
return s1.value, s2.value, s3.value
def plot_pdf(self, figsize=(8, 6)):
data = self.clip_stat_1
x_rs = self.cdf_x
y_hat = self.y_hat
point_mass_values = self.point_mass_locations
fig = plt.figure(figsize=figsize)
plt.hist(data[data > 0], bins=100, alpha=0.5, label="histogram")
scale = (np.histogram(data[data > 0], bins=100)[0].max() /
cvx.diff(y_hat, k=1).value.max())
plt.plot(
x_rs[:-1],
scale * cvx.diff(y_hat, k=1).value,
color="orange",
linewidth=1,
label="piecewise constant PDF estimate",
)
for count, val in enumerate(point_mass_values):
if count == 0:
plt.axvline(
val,
linewidth=1,
linestyle=":",
color="green",
label="detected point mass",
)
else:
plt.axvline(val, linewidth=1, linestyle=":", color="green")
return fig
def total_variation_plus_seasonal_filter(signal, c1=10, c2=500):
'''
This performs total variation filtering with the addition of a seasonal baseline fit. This introduces a new
signal to the model that is smooth and periodic on a yearly time frame. This does a better job of describing real,
multi-year solar PV power data sets, and therefore does an improved job of estimating the discretely changing
signal.
:param signal: A 1d numpy array (must support boolean indexing) containing the signal of interest
:param c1: The regularization parameter to control the total variation in the final output signal
:param c2: The regularization parameter to control the smoothness of the seasonal signal
:return: A 1d numpy array containing the filtered signal
'''
s_hat = cvx.Variable(len(signal))
s_seas = cvx.Variable(len(signal))
s_error = cvx.Variable(len(signal))
c1 = cvx.Constant(value=c1)
c2 = cvx.Constant(value=c2)
index_set = ~np.isnan(signal)
w = len(signal) / np.sum(index_set)
objective = cvx.Minimize((365 * 3 / len(signal)) * w *
cvx.sum(cvx.huber(s_error)) +
c1 * cvx.norm1(cvx.diff(s_hat, k=1)) +
c2 * cvx.norm(cvx.diff(s_seas, k=2)) +
c2 * .1 * cvx.norm(cvx.diff(s_seas, k=1)))
constraints = [
signal[index_set] == s_hat[index_set] + s_seas[index_set] +
s_error[index_set], s_seas[365:] - s_seas[:-365] == 0,
cvx.sum(s_seas[:365]) == 0
]
problem = cvx.Problem(objective=objective, constraints=constraints)
problem.solve()
return s_hat.value, s_seas.value
def test_diff(self) -> None:
"""Test the diff atom.
"""
A = cp.Variable((20, 10))
B = np.zeros((20, 10))
self.assertEqual(cp.diff(A, axis=0).shape, np.diff(B, axis=0).shape)
self.assertEqual(cp.diff(A, axis=1).shape, np.diff(B, axis=1).shape)
def local_quantile_regression_with_seasonal(signal,
use_ixs=None,
tau=0.75,
c1=1e3,
solver='ECOS',
residual_weights=None,
tv_weights=None):
'''
https://colab.research.google.com/github/cvxgrp/cvx_short_course/blob/master/applications/quantile_regression.ipynb
:param signal: 1d numpy array
:param use_ixs: optional index set to apply cost function to
:param tau: float, parameter for quantile regression
:param c1: float
:param solver: string
:return: median fit with seasonal baseline removed
'''
if use_ixs is None:
use_ixs = np.arange(len(signal))
x = cvx.Variable(len(signal))
r = signal[use_ixs] - x[use_ixs]
objective = cvx.Minimize(
cvx.sum(0.5 * cvx.abs(r) + (tau - 0.5) * r) +
c1 * cvx.norm(cvx.diff(x, k=2)))
if len(signal) > 365:
constraints = [x[365:] == x[:-365]]
else:
constraints = []
prob = cvx.Problem(objective, constraints=constraints)
prob.solve(solver=solver)
return x.value
def local_median_regression_with_seasonal(signal,
use_ixs=None,
c1=1e3,
solver='ECOS'):
'''
for a list of available solvers, see:
https://www.cvxpy.org/tutorial/advanced/index.html#solve-method-options
:param signal: 1d numpy array
:param use_ixs: optional index set to apply cost function to
:param c1: float
:param solver: string
:return: median fit with seasonal baseline removed
'''
if use_ixs is None:
use_ixs = np.arange(len(signal))
x = cvx.Variable(len(signal))
objective = cvx.Minimize(
cvx.norm1(signal[use_ixs] - x[use_ixs]) +
c1 * cvx.norm(cvx.diff(x, k=2)))
if len(signal) > 365:
constraints = [x[365:] == x[:-365]]
else:
constraints = []
prob = cvx.Problem(objective, constraints=constraints)
prob.solve(solver=solver)
return x.value
def obtain_component_r0(self, initial_r_cs_value, index_set=None):
"""
Obtains the initial r0 values that are used in place of variables
denominator of degradation equation.
Removed duplicated code from the original implementation.
Arguments
-----------------
initial_r_cs_value : numpy array
Initial low dimension right matrix.
Returns
-------
numpy array
The values that is used in order to make the constraint of
degradation to be linear.
"""
component_r0 = initial_r_cs_value[0]
if index_set is None:
index_set = component_r0 > 1e-3 * np.percentile(component_r0, 95)
x = cvx.Variable(initial_r_cs_value.shape[1])
objective = cvx.Minimize(
cvx.sum(0.5 * cvx.abs(component_r0[index_set] - x[index_set]) + (.9 - 0.5) *
(component_r0[index_set] - x[index_set])) + 1e3 * cvx.norm(cvx.diff(x, k=2)))
problem = cvx.Problem(objective)
problem.solve(solver=self._solver_type)
result_component_r0 = x.value
return result_component_r0
def tl1_l2d2p365(
signal,
use_ixs=None,
tau=0.75,
c1=1e3,
solver=None,
yearly_periodic=True,
verbose=False,
residual_weights=None,
tv_weights=None,
):
"""
https://colab.research.google.com/github/cvxgrp/cvx_short_course/blob/master/applications/quantile_regression.ipynb
:param signal: 1d numpy array
:param use_ixs: optional index set to apply cost function to
:param tau: float, parameter for quantile regression
:param c1: float
:param solver: string
:return: median fit with seasonal baseline removed
"""
if use_ixs is None:
use_ixs = ~np.isnan(signal)
x = cvx.Variable(len(signal))
r = signal[use_ixs] - x[use_ixs]
objective = cvx.Minimize(
cvx.sum(0.5 * cvx.abs(r) + (tau - 0.5) * r) +
c1 * cvx.norm(cvx.diff(x, k=2)))
if len(signal) > 365 and yearly_periodic:
constraints = [x[365:] == x[:-365]]
else:
constraints = []
prob = cvx.Problem(objective, constraints=constraints)
prob.solve(solver=solver, verbose=verbose)
return x.value
def l1_l2d2p365(signal,
use_ixs=None,
c1=1e3,
yearly_periodic=True,
solver=None,
verbose=False):
"""
for a list of available solvers, see:
https://www.cvxpy.org/tutorial/advanced/index.html#solve-method-options
:param signal: 1d numpy array
:param use_ixs: optional index set to apply cost function to
:param c1: float
:param solver: string
:return: median fit with seasonal baseline removed
"""
if use_ixs is None:
use_ixs = np.arange(len(signal))
x = cvx.Variable(len(signal))
objective = cvx.Minimize(
cvx.norm1(signal[use_ixs] - x[use_ixs]) +
c1 * cvx.norm(cvx.diff(x, k=2)))
if len(signal) > 365 and yearly_periodic:
constraints = [x[365:] == x[:-365]]
else:
constraints = []
prob = cvx.Problem(objective, constraints=constraints)
# Currently seems to work with SCS or MOSEK
prob.solve(solver=solver, verbose=verbose)
return x.value
def quad_solve(y, lambd):
x = cp.Variable(len(y))
loss = 0.5 * cp.sum_entries(cp.square(y - x)) + lambd * cp.norm1(
cp.diff(x))
cp.Problem(cp.Minimize(loss), [x[0] == y[0]]).solve()
return x.value.A[:, 0]
def find_clear_days(data, th=0.1, boolean_out=True):
'''
This function quickly finds clear days in a PV power data set. The input to this function is a 2D array containing
standardized time series power data. This will typically be the output from
`solardatatools.data_transforms.make_2d`. The filter relies on two estimates of daily "clearness": the smoothness
of each daily signal as measured by the l2-norm of the 2nd order difference, and seasonally-adjusted daily
energy. Seasonal adjustment of the daily energy if obtained by solving a local quantile regression problem, which
is a convex optimization problem and is solvable with cvxpy. The parameter `th` controls the relative weighting of
the daily smoothness and daily energy in the final filter in a geometric mean. A value of 0 will rely entirely on
the daily energy and a value of 1 will rely entirely on daily smoothness.
:param D: A 2D numpy array containing a solar power time series signal.
:param th: A parameter that tunes the filter between relying of daily smoothness and daily energy
:return: A 1D boolean array, with `True` values corresponding to clear days in the data set
'''
# Take the norm of the second different of each day's signal. This gives a rough estimate of the smoothness of
# day in the data set
tc = np.linalg.norm(data[:-2] - 2 * data[1:-1] + data[2:], ord=1, axis=0)
# Shift this metric so the median is at zero
tc = np.percentile(tc, 50) - tc
# Normalize such that the maximum value is equal to one
tc /= np.max(tc)
# Take the positive part function, i.e. set the negative values to zero. This is the first metric
tc = np.clip(tc, 0, None)
# Calculate the daily energy
de = np.sum(data, axis=0)
# Solve a convex minimization problem to roughly fit the local 90th percentile of the data (quantile regression)
x = cvx.Variable(len(tc))
obj = cvx.Minimize(
cvx.sum(0.5 * cvx.abs(de - x) + (.9 - 0.5) * (de - x)) +
1e3 * cvx.norm(cvx.diff(x, k=2)))
prob = cvx.Problem(obj)
try:
prob.solve(solver='MOSEK')
except Exception as e:
print(e)
print('Trying ECOS solver')
prob.solve(solver='ECOS')
# x gives us the local top 90th percentile of daily energy, i.e. the very sunny days. This gives us our
# seasonal normalization.
de = np.clip(np.divide(de, x.value), 0, 1)
# Take geometric mean
weights = np.multiply(np.power(tc, th), np.power(de, 1. - th))
# Set values less than 0.6 to be equal to zero
weights[weights < 0.6] = 0.
# Apply filter for sparsity to catch data errors related to non-zero nighttime data
try:
msk = filter_for_sparsity(data, solver='MOSEK')
except Exception as e:
print(e)
print('Trying ECOS solver')
msk = filter_for_sparsity(data, solver='ECOS')
weights = weights * msk.astype(int)
if boolean_out:
return weights >= 1e-3
else:
return weights
def make_l2_ll1d1(y, weight=1e1):
y_hat = cvx.Variable(len(y))
y_param = cvx.Parameter(len(y), value=y)
mu = cvx.Parameter(nonneg=True)
mu.value = weight
error = cvx.sum_squares(y_param - y_hat)
reg = cvx.norm(cvx.diff(y_hat, k=2), p=1)
objective = cvx.Minimize(error + mu * reg)
constraints = [y_param[0] == y_hat[0], y[-1] == y_hat[-1]]
problem = cvx.Problem(objective, constraints)
return problem, y_param, y_hat, mu
def lasso_solve(input_file, lambdas, pnorm=2):
toy = np.loadtxt(open(input_file, "rb"), delimiter=",")
m, n = toy.shape
nval = len(lambdas)
fig = plt.figure(figsize=(8, 8))
figr = math.ceil((1 + nval * 2) / 5)
figc = min(5, 1 + nval * 2)
plt.subplots_adjust(hspace=0.3)
plot = fig.add_subplot(figr, figc, 1)
plot.set_title("Original")
plt.imshow(toy)
theta = cp.Variable(toy.shape)
lambd = cp.Parameter(nonneg=True)
hdiff = cp.diff(theta, axis=0)
vdiff = cp.diff(theta, axis=1)
hdiff_flat = cp.reshape(hdiff[0:n - 1, 0:m - 1], ((n - 1) * (m - 1), ))
vdiff_flat = cp.reshape(vdiff[0:n - 1, 0:m - 1], ((n - 1) * (m - 1), ))
stack = cp.vstack([hdiff_flat, vdiff_flat])
objective = cp.sum_squares(toy - theta) / 2 + lambd * cp.sum(
cp.norm(stack, p=pnorm, axis=0))
problem = cp.Problem(cp.Minimize(objective))
for idx, val in enumerate(lambdas):
lambd.value = val
problem.solve(warm_start=True)
print("Lambda: {}, objective value: {:.2f}".format(val, problem.value))
plot = fig.add_subplot(figr, figc, 2 + 2 * idx)
plot.set_title("Lambda {}".format(val))
plt.imshow(theta.value)
# histogram
plot = fig.add_subplot(figr, figc, 3 + 2 * idx)
plt.hist(theta.value, bins=100, histtype='step')
plt.show()
def constraints(self):
P = self.terminals[0].power_var
if self.energy is None:
self.energy = cvx.Variable(self.terminals[0].power_var.shape)
e_init = cvx.reshape(self.energy_init, ())
constr = [
cvx.diff(self.energy.T) == P[1:, :] * self.len_interval,
self.energy[0, :] - e_init - P[0, :] * self.len_interval == 0,
self.terminals[0].power_var >= -self.discharge_max,
self.terminals[0].power_var <= self.charge_max,
self.energy <= self.energy_max,
self.energy >= 0,
]
if self.energy_final is not None:
constr += [self.energy[-1] >= self.energy_final]
return constr
def baseline_rope(y, lam=1):
"""
Baseline drift correction based on [1]
Inputs:
y: row signal to be cleaned (array, numpy array)
lam: reg. parameter (int)
Problem to Solve min |y-b| + lam*(diff_b)^2, s.t. b<=y
[1] Xie, Z., Schwartz, O., & Prasad, A. (2018). Decoding of finger
trajectory from ECoG using deep learning. Journal of neural engineering,
15(3), 036009.
"""
b = cp.Variable(y.shape)
objective = cp.Minimize(
cp.norm(y - b, 2) + lam * cp.sum_squares(cp.diff(b, 1)))
constraints = [b <= y]
problem = cp.Problem(objective, constraints)
problem.solve(solver="SCS")
z = b.value #--> baseline
return z
def plot_diffs(self, figsize=(8, 6)):
x_rs = self.cdf_x
metric = self.metric
threshold = self.threshold
y_hat = self.y_hat
point_masses = self.point_masses
point_mass_values = self.point_mass_locations
fig, ax = plt.subplots(nrows=2, sharex=True, figsize=figsize)
y1 = cvx.diff(y_hat, k=1).value
y2 = metric
ax[0].plot(x_rs[:-1], y1)
ax[1].plot(x_rs[1:-1], y2)
ax[1].axhline(threshold,
linewidth=1,
color="r",
ls=":",
label="decision boundary")
if len(point_mass_values) > 0:
ax[0].scatter(
x_rs[point_masses],
y1[point_masses[1:]],
color="red",
marker="o",
label="detected point mass",
)
ax[1].scatter(
x_rs[point_masses],
y2[point_masses[1:-1]],
color="red",
marker="o",
label="detected point mass",
)
ax[0].set_title("1st order difference of CDF fit")
ax[1].set_title("2nd order difference of CDF fit")
ax[1].legend()
plt.tight_layout()
return fig
def total_variation_filter(signal, C=5):
'''
This function performs total variation filtering or denoising on a 1D signal. This filter is implemented as a
convex optimization problem which is solved with cvxpy.
(https://en.wikipedia.org/wiki/Total_variation_denoising)
:param signal: A 1d numpy array (must support boolean indexing) containing the signal of interest
:param C: The regularization parameter to control the total variation in the final output signal
:return: A 1d numpy array containing the filtered signal
'''
s_hat = cvx.Variable(len(signal))
mu = cvx.Constant(value=C)
index_set = ~np.isnan(signal)
objective = cvx.Minimize(
cvx.sum(cvx.huber(signal[index_set] - s_hat[index_set])) +
mu * cvx.norm1(cvx.diff(s_hat, k=1)))
problem = cvx.Problem(objective=objective)
try:
problem.solve(solver='MOSEK')
except Exception as e:
print(e)
print('Trying ECOS solver')
problem.solve(solver='ECOS')
return s_hat.value
def updateSourceObj(self, sourcename):
import cvxpy as cvp #cvxpy is a bad bad no good library and we only want to use it when we're using a model using this solver.
if sourcename.lower() == 'all':
for name in self.models.keys():
self.updateSourceObj(name)
else:
model = self.models[sourcename]
## Define objective function to fit model to regressors
## **CHANGE MT: I moved the alpha variable to be inside the norms so that
## it can be time varying. I'm adding a check above to ensure that alpha is
## a scalar or a vector of length N.
if model['costFunction'].lower() == 'sse':
residuals = (model['source'] - model['regressor'] * model['theta'])
modelObj = cvp.sum_squares( cvp.mul_elemwise( model['alpha'] ** .5 , residuals ) )
elif model['costFunction'].lower() == 'l1':
residuals = (model['source'] - model['regressor'] * model['theta'])
modelObj = cvp.norm( cvp.mul_elemwise( model['alpha'] , residuals ) ,1)
elif model['costFunction'].lower()=='l2':
residuals = (model['source'] - model['regressor'] * model['theta'])
modelObj = cvp.norm( cvp.mul_elemwise( model['alpha'] , residuals ) ,2)
else:
raise ValueError('{} wrong option, use "sse","l2" or "l1"'.format(costFunction))
## Define cost function to regularize theta ****************
# ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** *
# Check that beta is scalar or of length of number of parameters.
model['beta'] = np.array(model['beta'])
if model['beta'].size not in [1, model['order']]:
raise ValueError('Beta must be scalar or vector with one element for each regressor')
if model['regularizeTheta'] is not None:
if callable(model['regularizeTheta']):
## User can input their own function to regularize theta.
# Must input a cvxpy variable vector and output a scalar
# or a vector with one element for each parameter.
## TODO: TRY CATCH TO ENSURE regularizeTheta WORKS AND RETURNS SCALAR
try:
regThetaObj = model['regularizeTheta'](model['theta']) * model['beta']
except:
raise ValueError('Check custom regularizer for model {}'.format(model['name']))
if regThetaObj.size[0]* regThetaObj.size[1] != 1:
raise ValueError('Check custom regularizer for model {}, make sure it returns a scalar'.format(model['name']))
elif model['regularizeTheta'].lower() == 'l2':
## Sum square errors.
regThetaObj = cvp.norm(model['theta'] * model['beta'])
elif model['regularizeTheta'].lower() == 'l1':
regThetaObj = cvp.norm(model['theta'] * model['beta'], 1)
else:
regThetaObj = 0
## Define cost function to regularize source signal ****************
# ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** *
# Check that gamma is scalar
model['gamma'] = np.array(model['gamma'])
if model['gamma'].size != 1:
raise NameError('Gamma must be scalar')
## Calculate regularization.
if model['regularizeSource'] is not None:
if callable(model['regularizeSource']):
## User can input their own function to regularize the source signal.
# Must input a cvxpy variable vector and output a scalar.
regSourceObj = model['regularizeSource'](model['source']) * model['gamma']
elif model['regularizeSource'].lower() == 'diff1_ss':
regSourceObj = cvp.sum_squares(cvp.diff(model['source'])) * model['gamma']
else:
raise Exception('regularizeSource must be a callable method, \`diff1_ss\`, or None')
else:
regSourceObj = 0
## Sum total model objective
model['obj'] = modelObj + regThetaObj + regSourceObj
## Append model to models list
self.models[sourcename] = model
[[[20, 8, 5, 2],
[8, 16, 2, 4],
[5, 2, 5, 2],
[2, 4, 2, 4]]], Constant([7.7424020218157814])),
(cp.geo_mean, tuple(), [[4, 1]], Constant([2])),
(cp.geo_mean, tuple(), [[0.01, 7]], Constant([0.2645751311064591])),
(cp.geo_mean, tuple(), [[63, 7]], Constant([21])),
(cp.geo_mean, tuple(), [[1, 10]], Constant([math.sqrt(10)])),
(lambda x: cp.geo_mean(x, [1, 1]), tuple(), [[1, 10]], Constant([math.sqrt(10)])),
(lambda x: cp.geo_mean(x, [.4, .8, 4.9]), tuple(),
[[.5, 1.8, 17]], Constant([10.04921378316062])),
(cp.harmonic_mean, tuple(), [[1, 2, 3]], Constant([1.6363636363636365])),
(cp.harmonic_mean, tuple(), [[2.5, 2.5, 2.5, 2.5]], Constant([2.5])),
(cp.harmonic_mean, tuple(), [[0, 1, 2]], Constant([0])),
(lambda x: cp.diff(x, 0), (3,), [[1, 2, 3]], Constant([1, 2, 3])),
(cp.diff, (2,), [[1, 2, 3]], Constant([1, 1])),
(cp.diff, tuple(), [[1.1, 2.3]], Constant([1.2])),
(lambda x: cp.diff(x, 2), tuple(), [[1, 2, 3]], Constant([0])),
(cp.diff, (3,), [[2.1, 1, 4.5, -.1]], Constant([-1.1, 3.5, -4.6])),
(lambda x: cp.diff(x, 2), (2,), [[2.1, 1, 4.5, -.1]], Constant([4.6, -8.1])),
(lambda x: cp.diff(x, 1, axis=0), (1, 2), [[[-5, -3], [2, 1]]],
Constant([[7], [4]])),
(lambda x: cp.diff(x, 1, axis=1), (2, 1), [[[-5, -3], [2, 1]]],
Constant([[2, -1]])),
(lambda x: cp.pnorm(x, .5), tuple(), [[1.1, 2, .1]], Constant([7.724231543909264])),
(lambda x: cp.pnorm(x, -.4), tuple(), [[1.1, 2, .1]], Constant([0.02713620334])),
(lambda x: cp.pnorm(x, -1), tuple(), [[1.1, 2, .1]], Constant([0.0876494023904])),
(lambda x: cp.pnorm(x, -2.3), tuple(), [[1.1, 2, .1]], Constant([0.099781528576])),
(cp.entr, (2, 2), [[[1, math.e], [math.e**2, 1.0 / math.e]]],
Constant([[0, -math.e], [-2 * math.e**2, 1.0 / math.e]])),
(cp.log_det, tuple(), [[[20, 8, 5, 2], [8, 16, 2, 4], [5, 2, 5, 2],
[2, 4, 2, 4]]], Constant([7.7424020218157814])),
(cp.geo_mean, tuple(), [[4, 1]], Constant([2])),
(cp.geo_mean, tuple(), [[0.01, 7]], Constant([0.2645751311064591])),
(cp.geo_mean, tuple(), [[63, 7]], Constant([21])),
(cp.geo_mean, tuple(), [[1, 10]], Constant([math.sqrt(10)])),
(lambda x: cp.geo_mean(x, [1, 1]), tuple(), [[1, 10]],
Constant([math.sqrt(10)])),
(lambda x: cp.geo_mean(x, [.4, .8, 4.9]), tuple(), [[.5, 1.8, 17]],
Constant([10.04921378316062])),
(cp.harmonic_mean, tuple(), [[1, 2, 3]], Constant([1.6363636363636365])),
(cp.harmonic_mean, tuple(), [[2.5, 2.5, 2.5, 2.5]], Constant([2.5])),
(cp.harmonic_mean, tuple(), [[0, 1, 2]], Constant([0])),
(lambda x: cp.diff(x, 0), (3, ), [[1, 2, 3]], Constant([1, 2, 3])),
(cp.diff, (2, ), [[1, 2, 3]], Constant([1, 1])),
(cp.diff, tuple(), [[1.1, 2.3]], Constant([1.2])),
(lambda x: cp.diff(x, 2), tuple(), [[1, 2, 3]], Constant([0])),
(cp.diff, (3, ), [[2.1, 1, 4.5, -.1]], Constant([-1.1, 3.5, -4.6])),
(lambda x: cp.diff(x, 2), (2, ), [[2.1, 1, 4.5,
-.1]], Constant([4.6, -8.1])),
(lambda x: cp.diff(x, 1, axis=0), (1, 2), [np.array([[-5, -3], [2, 1]])],
Constant([[7], [4]])),
(lambda x: cp.diff(x, 1, axis=1), (2, 1), [np.array([[-5, -3], [2, 1]])],
Constant([[2, -1]])),
(lambda x: cp.pnorm(x, .5), tuple(), [[1.1, 2, .1]],
Constant([7.724231543909264])),
(lambda x: cp.pnorm(x, -.4), tuple(), [[1.1, 2,
.1]], Constant([0.02713620334])),
(lambda x: cp.pnorm(x, -1), tuple(), [[1.1, 2,
l = cvx.Variable(T)
v = cvx.Variable(T)
Val = cvx.Variable(T)
Bal = cvx.Variable(T)
a = cvx.Variable(1)
b = cvx.Variable(1)
turb = cvx.Variable(T)
const = [
l[1:] == l[:-1] + dl[:-1] + Bal[:-1] + Val[:-1],
v[1:] == v[:-1] + 1 / klin / 4 * 60 * (l[:-1] - v[:-1]) + turb[:-1],
h[1:] == h[:-1] + v[:-1]
]
const.append(Bal == a * bal2)
const.append(Val == a * val2)
obj = cvx.Minimize(0.001 * cvx.sum_squares(turb) +
100 * cvx.sum_squares(cvx.diff(dl)))
prob = cvx.Problem(obj, const)
r = prob.solve(
) #solver = 'ECOS',verbose=True,reltol=1e-10,abstol=1e-10,max_iters=200)
print(r)
temps = df.atmo_temp_dp[::intv]
l = np.asarray(l.value.T)[0, :]
dl = np.asarray(dl.value.T)[0, :]
#plt.plot(temps, np.sign(l)*)dl)
#m, b = np.polyfit(temps, np.sign(l)*(np.asarray(l.value.T).T), 1)
#plt.plot(np.unique(temps), m*np.unique(temps)+b)
plt.xlabel('gradient (dK/dPa)')
plt.ylabel('dl/dt')
plt.show()
def l1filter(t,
y,
lam=1200,
rho=80,
periods=(365.25, 182.625),
solver=cvx.MOSEK,
verbose=False):
"""
Do l1 regularize for given time series.
:param t: np.array, time
:param y: np.array, time series value
:param lam: lambda value
:param rho: rho value
:param periods: list, periods, same unit as t
:param solver: cvx.solver
:param verbose: bool, show verbose or not
:return: x, w, s, if periods is not None, else return x, w
"""
t = np.asarray(t, dtype=np.float64)
t = t - t[0]
y = np.asarray(y, dtype=np.float64)
assert y.shape == t.shape
n = len(t)
D = gen_d2(n)
x = cvx.Variable(n)
w = cvx.Variable(n)
errs = y - x - w
seasonal = None
if periods:
tpi_t = 2 * np.pi * t
for period in periods:
a = cvx.Variable()
b = cvx.Variable()
temp = a * np.sin(tpi_t / period) + b * np.cos(tpi_t / period)
if seasonal is None:
seasonal = temp
else:
seasonal += temp
errs = errs - seasonal
obj = cvx.Minimize(0.5 * cvx.sum_squares(errs) + lam * cvx.norm(D * x, 1) +
rho * cvx.tv(w))
prob = cvx.Problem(obj)
prob.solve(solver=solver, verbose=verbose)
if periods:
return np.array(x.value), np.array(w.value), np.array(seasonal.value)
else:
return np.array(x.value), np.array(w.value), None
t = np.asarray(t, dtype=np.float64)
y = np.asarray(y, dtype=np.float64)
n = len(t)
x = cvx.Variable(n)
w = cvx.Variable(n)
dx = cvx.mul_elemwise(1.0 / np.diff(t), cvx.diff(x))
x_term = cvx.tv(dx)
dw = cvx.mul_elemwise(1.0 / np.diff(t), cvx.diff(w))
w_term = cvx.norm(dw, 1)
errs = y - x - w
seasonal = None
if periods:
tpi_t = 2 * np.pi * t
for period in periods:
a = cvx.Variable()
b = cvx.Variable()
temp = a * np.sin(tpi_t / period) + b * np.cos(tpi_t / period)
if seasonal is None:
seasonal = temp
else:
seasonal += temp
errs = errs - seasonal
obj = cvx.Minimize(0.5 * cvx.sum_squares(errs) + lam * x_term +
rho * w_term)
prob = cvx.Problem(obj)
prob.solve(solver=solver, verbose=verbose)
if periods:
return np.array(x.value), np.array(w.value), np.array(seasonal.value)
else:
return np.array(x.value), np.array(w.value), None
# create problem data
N = 100;
# create an increasing input signal
xtrue = np.zeros((N,1))
xtrue[1:40] = 0.1
xtrue[50] = 2
xtrue[70:80] = 0.15
xtrue[80] = 1
xtrue = np.cumsum(xtrue)
# pass the increasing input through a moving-average filter
# and add Gaussian noise
h = np.array([1, -0.85 ,0.7 ,-0.3])
k = h.shape[0]
yhat = np.convolve(h,xtrue)
y = yhat[:-3] + np.random.randn(N)
x = cp.Variable((100,),nonneg = True)
z = y[:,None] - cp.conv(h,x)[:-3]
objective = cp.Minimize(cp.sum_squares(z))
constraints = [cp.diff(x) >= 0]
prob=cp.Problem(objective,constraints=constraints)
prob.solve()
#plot
t = list(range(0,xtrue.size))
plt.plot(t,list(xtrue), color='red',label='x_true')
plt.plot(t,list(x.value), color='blue',label='x_hat')
plt.legend(loc="upper left")
plt.savefig('prob_66.png')
plt.show()
def addSource(
self,
regressor,
name=None,
costFunction='sse',
alpha=1, # Cost function for fit to regressors, alpha is a scalar multiplier
regularizeTheta=None,
beta=1, # Cost function for parameter regularization, beta is a scalar multiplier
regularizeSource=None,
gamma=1, # Cost function for signal smoothing, gamma is a scalar multiplier
lb=None, # Lower bound on source
ub=None, # Upper bound on source
idxScrReg=None, # indices used to break the source signal into smaller ones to apply regularization
numWind=1, # number of time windows (relevant for time-varying regressors)
):
### This is a method to add a new source
self.modelcounter += 1 # Increment model counter
## Write model name if it doesn't exist.
if name is None:
name = str(self.modelcounter)
## Instantiate a dictionary of model terms
model = {}
model['name'] = name
model['alpha'] = alpha
model['lb'] = lb
model['ub'] = ub
## Check regressor shape
regressor = np.array(regressor)
if regressor.ndim == 0: ## If no regressors are included, set them an empty array
regressor = np.zeros((self.N, 0))
if regressor.ndim == 1:
regressor = np.expand_dims(regressor, 1)
if regressor.ndim > 2:
raise NameError('Regressors cannot have more than 2 dimensions')
## Check that regressors have the correct shape (Nobs, Nregressors)
if regressor.shape[0] != self.N:
if regressor.shape[1] == self.N:
regressor = regressor.transpose()
else:
raise NameError(
'Lengths of regressors and aggregate signal must match')
## Define model regressors and order
model['regressor'] = regressor
model['order'] = regressor.shape[1]
## Define decision variables and cost function style
model['source'] = cvp.Variable(self.N, 1)
#model['source'] = cvp.Variable((self.N,1)) # required for cvxpy 1.0.1
model['theta'] = cvp.Variable(model['order'], 1)
#model['theta'] = cvp.Variable((model['order'],1)) # required for cvxpy 1.0.1
model['costFunction'] = costFunction
## Define objective function to fit model to regressors
if costFunction.lower() == 'sse':
residuals = (model['source'] - model['regressor'] * model['theta'])
modelObj = cvp.sum_squares(residuals) * model['alpha']
elif costFunction.lower() == 'l1':
residuals = (model['source'] - model['regressor'] * model['theta'])
#residuals = (model['source'] - auxVec - model['regressor'] * model['theta'])
modelObj = cvp.norm(residuals, 1) * model['alpha']
elif costFunction.lower() == 'l2':
residuals = (model['source'] - model['regressor'] * model['theta'])
modelObj = cvp.norm(residuals, 2) * model['alpha']
else:
raise ValueError(
'{} wrong option, use "sse","l2" or "l1"'.format(costFunction))
## Define cost function to regularize theta ****************
# ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** *
# Check that beta is scalar or of length of number of parameters.
beta = np.array(beta)
if beta.size not in [1, model['order']]:
raise ValueError(
'Beta must be scalar or vector with one element for each regressor'
)
if regularizeTheta is not None:
if callable(regularizeTheta):
## User can input their own function to regularize theta.
# Must input a cvxpy variable vector and output a scalar
# or a vector with one element for each parameter.
try:
regThetaObj = regularizeTheta(model['theta']) * beta
except:
raise ValueError(
'Check custom regularizer for model {}'.format(
model['name']))
if regThetaObj.size[0] * regThetaObj.size[1] != 1:
raise ValueError(
'Check custom regularizer for model {}, make sure it returns a scalar'
.format(model['name']))
elif regularizeTheta.lower() == 'l2':
## Sum square errors.
regThetaObj = cvp.norm(model['theta'] * beta)
elif regularizeTheta.lower() == 'l1':
regThetaObj = cvp.norm(model['theta'] * beta, 1)
elif regularizeTheta.lower() == 'diff_l2':
if numWind == 1:
regThetaObj = 0
else:
if regressor.shape[
1] == numWind: # this actually corresponds to the solar model (no intercept)
thetaDiffVec = cvp.diff(model['theta'])
else:
thetaDiffVec = cvp.vstack(
cvp.diff(model['theta'][0:numWind]),
cvp.diff(model['theta'][numWind:2 * numWind]))
regThetaObj = cvp.norm(thetaDiffVec, 2) * beta
elif regularizeTheta.lower() == 'diff_l1':
if numWind == 1:
regThetaObj = 0
else:
if regressor.shape[
1] == numWind: # this actually corresponds to the solar model (no intercept)
thetaDiffVec = cvp.diff(model['theta'])
else:
thetaDiffVec = cvp.vstack(
cvp.diff(model['theta'][0:numWind]),
cvp.diff(model['theta'][numWind:2 * numWind]))
regThetaObj = cvp.norm(thetaDiffVec, 1) * beta
else:
regThetaObj = 0
## Define cost function to regularize source signal ****************
# ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** *
# Check that gamma is scalar
gamma = np.array(gamma)
if gamma.size != 1:
raise NameError('Gamma must be scalar')
## Calculate regularization.
if regularizeSource is not None:
if idxScrReg is not None:
scrVec = model['source'][0:idxScrReg[0]]
idxStart = idxScrReg[0] + 1
for idxEnd in idxScrReg[1:]:
scrCur = cvp.diff(model['source'][idxStart:idxEnd])
scrVec = cvp.vstack(scrVec, scrCur)
idxStart = idxEnd + 1
scrVec = cvp.vstack(scrVec,
cvp.diff(model['source'][idxStart:]))
else:
scrVec = cvp.diff(model['source'])
if callable(regularizeSource):
## User can input their own function to regularize the source signal.
# Must input a cvxpy variable vector and output a scalar.
regSourceObj = regularizeSource(scrVec) * gamma
elif regularizeSource.lower() == 'diff1_ss':
regSourceObj = cvp.sum_squares(scrVec) * gamma
elif regularizeSource.lower() == 'diff_l1':
regSourceObj = cvp.norm(scrVec, 1) * gamma
elif regularizeSource.lower() == 'diff_l2':
regSourceObj = cvp.norm(scrVec, 2) * gamma
else:
regSourceObj = 0
## Sum total model objective
model['obj'] = modelObj + regThetaObj + regSourceObj
## Append model to models list
self.models[name] = model
def objective_constraints(self,
variables,
mask,
reservations,
mpc_ene=None):
""" Builds the master constraint list for the subset of timeseries data being optimized.
Args:
variables (Dict): Dictionary of variables being optimized
mask (DataFrame): A boolean array that is true for indices corresponding to time_series data included
in the subs data set
reservations (Dict): Dictionary of energy and power reservations required by the services being
preformed with the current optimization subset
mpc_ene (float): value of energy at end of last opt step (for mpc opt)
Returns:
A list of constraints that corresponds the battery's physical constraints and its service constraints
"""
constraint_list = []
size = int(np.sum(mask))
curr_e_cap = self.physical_constraints['ene_max_rated'].value
ene_target = self.soc_target * curr_e_cap
# optimization variables
ene = variables['ene']
dis = variables['dis']
ch = variables['ch']
on_c = variables['on_c']
on_d = variables['on_d']
try:
pv_gen = variables['pv_out']
except KeyError:
pv_gen = np.zeros(size)
try:
ice_gen = variables['ice_gen']
except KeyError:
ice_gen = np.zeros(size)
# create cvx parameters of control constraints (this improves readability in cvx costs and better handling)
ene_max = cvx.Parameter(
size,
value=self.control_constraints['ene_max'].value[mask].values,
name='ene_max')
ene_min = cvx.Parameter(
size,
value=self.control_constraints['ene_min'].value[mask].values,
name='ene_min')
ch_max = cvx.Parameter(
size,
value=self.control_constraints['ch_max'].value[mask].values,
name='ch_max')
ch_min = cvx.Parameter(
size,
value=self.control_constraints['ch_min'].value[mask].values,
name='ch_min')
dis_max = cvx.Parameter(
size,
value=self.control_constraints['dis_max'].value[mask].values,
name='dis_max')
dis_min = cvx.Parameter(
size,
value=self.control_constraints['dis_min'].value[mask].values,
name='dis_min')
# energy at the end of the last time step (makes sure that the end of the last time step is ENE_TARGET
# TODO: rewrite this if MPC_ENE is not None
constraint_list += [
cvx.Zero((ene_target - ene[-1]) - (self.dt * ch[-1] * self.rte) +
(self.dt * dis[-1]) - reservations['E'][-1] +
(self.dt * ene[-1] * self.sdr * 0.01))
]
# energy generally for every time step
constraint_list += [
cvx.Zero(ene[1:] - ene[:-1] - (self.dt * ch[:-1] * self.rte) +
(self.dt * dis[:-1]) - reservations['E'][:-1] +
(self.dt * ene[:-1] * self.sdr * 0.01))
]
# energy at the beginning of the optimization window -- handles rolling window
if mpc_ene is None:
constraint_list += [cvx.Zero(ene[0] - ene_target)]
else:
constraint_list += [cvx.Zero(ene[0] - mpc_ene)]
# Keep energy in bounds determined in the constraints configuration function -- making sure our storage meets control constraints
constraint_list += [
cvx.NonPos(ene_target - ene_max[-1] + reservations['E_upper'][-1] -
variables['ene_max_slack'][-1])
]
constraint_list += [
cvx.NonPos(ene[:-1] - ene_max[:-1] + reservations['E_upper'][:-1] -
variables['ene_max_slack'][:-1])
]
constraint_list += [
cvx.NonPos(-ene_target + ene_min[-1] +
reservations['E_lower'][-1] -
variables['ene_min_slack'][-1])
]
constraint_list += [
cvx.NonPos(ene_min[1:] - ene[1:] + reservations['E_lower'][:-1] -
variables['ene_min_slack'][:-1])
]
# Keep charge and discharge power levels within bounds
constraint_list += [
cvx.NonPos(-ch_max + ch - dis + reservations['D_min'] +
reservations['C_max'] - variables['ch_max_slack'])
]
constraint_list += [
cvx.NonPos(-ch + dis + reservations['C_min'] +
reservations['D_max'] - dis_max -
variables['dis_max_slack'])
]
constraint_list += [cvx.NonPos(ch - cvx.multiply(ch_max, on_c))]
constraint_list += [cvx.NonPos(dis - cvx.multiply(dis_max, on_d))]
# removing the band in between ch_min and dis_min that the battery will not operate in
constraint_list += [
cvx.NonPos(
cvx.multiply(ch_min, on_c) - ch + reservations['C_min'])
]
constraint_list += [
cvx.NonPos(
cvx.multiply(dis_min, on_d) - dis + reservations['D_min'])
]
# the constraint below limits energy throughput and total discharge to less than or equal to
# (number of cycles * energy capacity) per day, for technology warranty purposes
# this constraint only applies when optimization window is equal to or greater than 24 hours
if self.daily_cycle_limit and size >= 24:
sub = mask.loc[mask]
for day in sub.index.dayofyear.unique():
day_mask = (day == sub.index.dayofyear)
constraint_list += [
cvx.NonPos(
cvx.sum(dis[day_mask] * self.dt +
cvx.pos(reservations['E'][day_mask])) -
self.ene_max_rated * self.daily_cycle_limit)
]
elif self.daily_cycle_limit and size < 24:
e_logger.info(
'Daily cycle limit did not apply as optimization window is less than 24 hours.'
)
# constraints to keep slack variables positive
if self.incl_slack:
constraint_list += [cvx.NonPos(-variables['ch_max_slack'])]
constraint_list += [cvx.NonPos(-variables['ch_min_slack'])]
constraint_list += [cvx.NonPos(-variables['dis_max_slack'])]
constraint_list += [cvx.NonPos(-variables['dis_min_slack'])]
constraint_list += [cvx.NonPos(-variables['ene_max_slack'])]
constraint_list += [cvx.NonPos(-variables['ene_min_slack'])]
if self.incl_binary:
# when dis_min or ch_min has been overwritten (read: increased) by predispatch services, need to force technology to be on
# TODO better way to do this???
ind_d = [
i for i in range(size)
if self.control_constraints['dis_min'].value[mask].values[i] >
self.physical_constraints['dis_min_rated'].value
]
ind_c = [
i for i in range(size)
if self.control_constraints['ch_min'].value[mask].values[i] >
self.physical_constraints['ch_min_rated'].value
]
if len(ind_d) > 0:
constraint_list += [on_d[ind_d] == 1] # np.ones(len(ind_d))
if len(ind_c) > 0:
constraint_list += [on_c[ind_c] == 1] # np.ones(len(ind_c))
# note: cannot operate startup without binary
if self.incl_startup:
# startup variables are positive
constraint_list += [cvx.NonPos(-variables['start_d'])]
constraint_list += [cvx.NonPos(-variables['start_c'])]
# difference between binary variables determine if started up in previous interval
constraint_list += [
cvx.NonPos(cvx.diff(on_d) - variables['start_d'][1:])
] # first variable not constrained
constraint_list += [
cvx.NonPos(cvx.diff(on_c) - variables['start_c'][1:])
] # first variable not constrained
return constraint_list
def get_params(data):
r"""Correct a signal estimated as numerator/denominator for weekday effects.
The ordinary estimate would be numerator_t/denominator_t for each time point
t. Instead, model
log(numerator_t/denominator_t) = alpha_{wd(t)} + phi_t
where alpha is a vector of fixed effects for each weekday. For
identifiability, we constrain \sum alpha_j = 0, and to enforce this we set
Sunday's fixed effect to be the negative sum of the other weekdays.
We estimate this as a penalized Poisson GLM problem with log link. We
rewrite the problem as
log(numerator_t) = alpha_{wd(t)} + phi_t + log(denominator_t)
and set a design matrix X with one row per time point. The first six columns
of X are weekday indicators; the remaining columns are the identity matrix,
so that each time point gets a unique phi. Using this X, we write
log(numerator_t) = X beta + log(denominator_t)
Hence the first six entries of beta correspond to alpha, and the remaining
entries to phi.
The penalty is on the L1 norm of third differences of phi (so the third
differences of the corresponding columns of beta), to enforce smoothness.
Third differences ensure smoothness without removing peaks or valleys.
Objective function is negative mean Poisson log likelihood plus penalty:
ll = (numerator * (X*b + log(denominator)) - sum(exp(X*b) + log(denominator)))
/ num_days
Return a matrix of parameters: the entire vector of betas, for each time
series column in the data.
"""
denoms = data.groupby(Config.DATE_COL).sum()["Denominator"]
nums = data.groupby(Config.DATE_COL).sum()[Config.CLI_COLS +
Config.FLU1_COL]
# Construct design matrix to have weekday indicator columns and then day
# indicators.
X = np.zeros((nums.shape[0], 6 + nums.shape[0]))
not_sunday = np.where(nums.index.dayofweek != 6)[0]
X[not_sunday, np.array(nums.index.dayofweek)[not_sunday]] = 1
X[np.where(nums.index.dayofweek == 6)[0], :6] = -1
X[:, 6:] = np.eye(X.shape[0])
npnums, npdenoms = np.array(nums), np.array(denoms)
params = np.zeros((nums.shape[1], X.shape[1]))
# Loop over the available numerator columns and smooth each separately.
for i in range(nums.shape[1]):
b = cp.Variable((X.shape[1]))
lmbda = cp.Parameter(nonneg=True)
lmbda.value = 10 # Hard-coded for now, seems robust to changes
ll = (cp.matmul(npnums[:, i],
cp.matmul(X, b) + np.log(npdenoms)) -
cp.sum(
cp.exp(cp.matmul(X, b) + np.log(npdenoms)))) / X.shape[0]
penalty = (lmbda * cp.norm(cp.diff(b[6:], 3), 1) / (X.shape[0] - 2)
) # L-1 Norm of third differences, rewards smoothness
try:
prob = cp.Problem(cp.Minimize(-ll + lmbda * penalty))
_ = prob.solve()
except:
# If the magnitude of the objective function is too large, an error is
# thrown; Rescale the objective function
prob = cp.Problem(cp.Minimize((-ll + lmbda * penalty) / 1e5))
_ = prob.solve()
params[i, :] = b.value
return params
def constraints(self, mask, sizing_for_rel=False, find_min_soe=False):
"""Default build constraint list method. Used by services that do not have constraints.
Args:
mask (DataFrame): A boolean array that is true for indices corresponding to time_series data included
in the subs data set
Returns:
A list of constraints that corresponds the battery's physical constraints and its service constraints
"""
constraint_list = []
size = int(np.sum(mask))
ene_target = self.soc_target * self.effective_soe_max # this is init_ene
# optimization variables
ene = self.variables_dict['ene']
dis = self.variables_dict['dis']
ch = self.variables_dict['ch']
uene = self.variables_dict['uene']
udis = self.variables_dict['udis']
uch = self.variables_dict['uch']
on_c = self.variables_dict['on_c']
on_d = self.variables_dict['on_d']
start_c = self.variables_dict['start_c']
start_d = self.variables_dict['start_d']
if sizing_for_rel:
constraint_list += [
cvx.Zero(ene[0] - ene_target + (self.dt * dis[0]) -
(self.rte * self.dt * ch[0]) - uene[0] +
(ene[0] * self.sdr * 0.01))
]
constraint_list += [
cvx.Zero(ene[1:] - ene[:-1] + (self.dt * dis[1:]) -
(self.rte * self.dt * ch[1:]) - uene[1:] +
(ene[1:] * self.sdr * 0.01))
]
else:
# energy at beginning of time step must be the target energy value
constraint_list += [cvx.Zero(ene[0] - ene_target)]
# energy evolution generally for every time step
constraint_list += [
cvx.Zero(ene[1:] - ene[:-1] + (self.dt * dis[:-1]) -
(self.rte * self.dt * ch[:-1]) - uene[:-1] +
(ene[:-1] * self.sdr * 0.01))
]
# energy at the end of the last time step (makes sure that the end of the last time step is ENE_TARGET
constraint_list += [
cvx.Zero(ene_target - ene[-1] + (self.dt * dis[-1]) -
(self.rte * self.dt * ch[-1]) - uene[-1] +
(ene[-1] * self.sdr * 0.01))
]
# constraints on the ch/dis power
constraint_list += [cvx.NonPos(ch - (on_c * self.ch_max_rated))]
constraint_list += [cvx.NonPos((on_c * self.ch_min_rated) - ch)]
constraint_list += [cvx.NonPos(dis - (on_d * self.dis_max_rated))]
constraint_list += [cvx.NonPos((on_d * self.dis_min_rated) - dis)]
# constraints on the state of energy
constraint_list += [cvx.NonPos(self.effective_soe_min - ene)]
constraint_list += [cvx.NonPos(ene - self.effective_soe_max)]
# account for -/+ sub-dt energy -- this is the change in energy that the battery experiences as a result of energy option
# if sizing for reliability
if sizing_for_rel:
constraint_list += [cvx.Zero(uene)]
else:
constraint_list += [
cvx.Zero(uene + (self.dt * udis) - (self.dt * uch * self.rte))
]
# the constraint below limits energy throughput and total discharge to less than or equal to
# (number of cycles * energy capacity) per day, for technology warranty purposes
# this constraint only applies when optimization window is equal to or greater than 24 hours
if self.daily_cycle_limit and size >= 24:
sub = mask.loc[mask]
for day in sub.index.dayofyear.unique():
day_mask = (day == sub.index.dayofyear)
constraint_list += [
cvx.NonPos(
cvx.sum(dis[day_mask] + udis[day_mask]) * self.dt -
self.ene_max_rated * self.daily_cycle_limit)
]
elif self.daily_cycle_limit and size < 24:
TellUser.info(
'Daily cycle limit did not apply as optimization window is less than 24 hours.'
)
# note: cannot operate startup without binary
if self.incl_startup and self.incl_binary:
# startup variables are positive
constraint_list += [cvx.NonPos(-start_c)]
constraint_list += [cvx.NonPos(-start_d)]
# difference between binary variables determine if started up in
# previous interval
constraint_list += [cvx.NonPos(cvx.diff(on_d) - start_d[1:])]
constraint_list += [cvx.NonPos(cvx.diff(on_c) - start_c[1:])]
return constraint_list
def total_variation_plus_seasonal_quantile_filter(signal,
use_ixs=None,
tau=0.995,
c1=1e3,
c2=1e2,
c3=1e2,
solver='ECOS',
residual_weights=None,
tv_weights=None):
'''
This performs total variation filtering with the addition of a seasonal baseline fit. This introduces a new
signal to the model that is smooth and periodic on a yearly time frame. This does a better job of describing real,
multi-year solar PV power data sets, and therefore does an improved job of estimating the discretely changing
signal.
:param signal: A 1d numpy array (must support boolean indexing) containing the signal of interest
:param c1: The regularization parameter to control the total variation in the final output signal
:param c2: The regularization parameter to control the smoothness of the seasonal signal
:return: A 1d numpy array containing the filtered signal
'''
n = len(signal)
if residual_weights is None:
residual_weights = np.ones_like(signal)
if tv_weights is None:
tv_weights = np.ones(len(signal) - 1)
if use_ixs is None:
use_ixs = np.ones(n, dtype=np.bool)
# selected_days = np.arange(n)[index_set]
# np.random.shuffle(selected_days)
# ix = 2 * n // 3
# train = selected_days[:ix]
# validate = selected_days[ix:]
# train.sort()
# validate.sort()
s_hat = cvx.Variable(n)
s_seas = cvx.Variable(max(n, 366))
s_error = cvx.Variable(n)
s_linear = cvx.Variable(n)
c1 = cvx.Parameter(value=c1, nonneg=True)
c2 = cvx.Parameter(value=c2, nonneg=True)
c3 = cvx.Parameter(value=c3, nonneg=True)
tau = cvx.Parameter(value=tau)
# w = len(signal) / np.sum(index_set)
beta = cvx.Variable()
objective = cvx.Minimize(
# (365 * 3 / len(signal)) * w * cvx.sum(0.5 * cvx.abs(s_error) + (tau - 0.5) * s_error)
2 * cvx.sum(0.5 * cvx.abs(cvx.multiply(residual_weights, s_error)) +
(tau - 0.5) * cvx.multiply(residual_weights, s_error)) +
c1 * cvx.norm1(cvx.multiply(tv_weights, cvx.diff(s_hat, k=1))) +
c2 * cvx.norm(cvx.diff(s_seas, k=2)) + c3 * beta**2)
constraints = [
signal[use_ixs] == s_hat[use_ixs] + s_seas[:n][use_ixs] +
s_error[use_ixs],
cvx.sum(s_seas[:365]) == 0
]
if True:
constraints.append(s_seas[365:] - s_seas[:-365] == beta)
constraints.extend([beta <= 0.01, beta >= -0.1])
problem = cvx.Problem(objective=objective, constraints=constraints)
problem.solve(solver='MOSEK')
return s_hat.value, s_seas.value[:n]
def total_variation_plus_seasonal_filter(signal,
c1=10,
c2=500,
residual_weights=None,
tv_weights=None,
use_ixs=None,
periodic_detector=False,
transition_locs=None,
seas_max=None):
'''
This performs total variation filtering with the addition of a seasonal baseline fit. This introduces a new
signal to the model that is smooth and periodic on a yearly time frame. This does a better job of describing real,
multi-year solar PV power data sets, and therefore does an improved job of estimating the discretely changing
signal.
:param signal: A 1d numpy array (must support boolean indexing) containing the signal of interest
:param c1: The regularization parameter to control the total variation in the final output signal
:param c2: The regularization parameter to control the smoothness of the seasonal signal
:return: A 1d numpy array containing the filtered signal
'''
if residual_weights is None:
residual_weights = np.ones_like(signal)
if tv_weights is None:
tv_weights = np.ones(len(signal) - 1)
if use_ixs is None:
index_set = ~np.isnan(signal)
else:
index_set = np.logical_and(use_ixs, ~np.isnan(signal))
s_hat = cvx.Variable(len(signal))
s_seas = cvx.Variable(len(signal))
s_error = cvx.Variable(len(signal))
c1 = cvx.Constant(value=c1)
c2 = cvx.Constant(value=c2)
#w = len(signal) / np.sum(index_set)
if transition_locs is None:
objective = cvx.Minimize(
# (365 * 3 / len(signal)) * w *
# cvx.sum(cvx.huber(cvx.multiply(residual_weights, s_error)))
10 * cvx.norm(cvx.multiply(residual_weights, s_error)) +
c1 * cvx.norm1(cvx.multiply(tv_weights, cvx.diff(s_hat, k=1))) +
c2 * cvx.norm(cvx.diff(s_seas, k=2))
# + c2 * .1 * cvx.norm(cvx.diff(s_seas, k=1))
)
else:
objective = cvx.Minimize(
10 * cvx.norm(cvx.multiply(residual_weights, s_error)) +
c2 * cvx.norm(cvx.diff(s_seas, k=2)))
constraints = [
signal[index_set] == s_hat[index_set] + s_seas[index_set] +
s_error[index_set],
cvx.sum(s_seas[:365]) == 0
]
if len(signal) > 365:
constraints.append(s_seas[365:] - s_seas[:-365] == 0)
if periodic_detector:
constraints.append(s_hat[365:] - s_hat[:-365] == 0)
if transition_locs is not None:
loc_mask = np.ones(len(signal) - 1, dtype=np.bool)
loc_mask[transition_locs] = False
# loc_mask[transition_locs + 1] = False
constraints.append(cvx.diff(s_hat, k=1)[loc_mask] == 0)
if seas_max is not None:
constraints.append(s_seas <= seas_max)
problem = cvx.Problem(objective=objective, constraints=constraints)
problem.solve()
return s_hat.value, s_seas.value
def addSource(self, regressor, name = None,
costFunction='sse',alpha = 1, # Cost function for fit to regressors, alpha is a scalar multiplier
regularizeTheta=None, beta = 1, # Cost function for parameter regularization, beta is a scalar multiplier
regularizeSource=None, gamma = 1, # Cost function for signal smoothing, gamma is a scalar multiplier
lb=None, # Lower bound on source
ub=None, # Upper bound on source
idxScrReg=None, # indices used to break the source signal into smaller ones to apply regularization
numWind=1, # number of time windows (relevant for time-varying regressors)
):
### This is a method to add a new source
self.modelcounter += 1 # Increment model counter
## Write model name if it doesn't exist.
if name is None:
name = str(self.modelcounter)
## Instantiate a dictionary of model terms
model = {}
model['name'] = name
model['alpha'] = alpha
model['lb']=lb
model['ub']=ub
## Check regressor shape
regressor = np.array(regressor)
if regressor.ndim == 0: ## If no regressors are included, set them an empty array
regressor = np.zeros((self.N,0))
if regressor.ndim == 1:
regressor = np.expand_dims(regressor,1)
if regressor.ndim > 2:
raise NameError('Regressors cannot have more than 2 dimensions')
## Check that regressors have the correct shape (Nobs, Nregressors)
if regressor.shape[0] != self.N:
if regressor.shape[1] == self.N:
regressor = regressor.transpose()
else:
raise NameError('Lengths of regressors and aggregate signal must match')
## Define model regressors and order
model['regressor'] = regressor
model['order'] = regressor.shape[1]
## Define decision variables and cost function style
model['source'] = cvp.Variable(self.N,1)
#model['source'] = cvp.Variable((self.N,1)) # required for cvxpy 1.0.1
model['theta'] = cvp.Variable(model['order'],1)
#model['theta'] = cvp.Variable((model['order'],1)) # required for cvxpy 1.0.1
model['costFunction'] = costFunction
## Define objective function to fit model to regressors
if costFunction.lower() == 'sse':
residuals = (model['source'] - model['regressor'] * model['theta'])
modelObj = cvp.sum_squares(residuals) * model['alpha']
elif costFunction.lower() == 'l1':
residuals = (model['source'] - model['regressor'] * model['theta'])
#residuals = (model['source'] - auxVec - model['regressor'] * model['theta'])
modelObj = cvp.norm(residuals,1) * model['alpha']
elif costFunction.lower()=='l2':
residuals = (model['source'] - model['regressor'] * model['theta'])
modelObj = cvp.norm(residuals,2) * model['alpha']
else:
raise ValueError('{} wrong option, use "sse","l2" or "l1"'.format(costFunction))
## Define cost function to regularize theta ****************
# ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** *
# Check that beta is scalar or of length of number of parameters.
beta = np.array(beta)
if beta.size not in [1, model['order']]:
raise ValueError('Beta must be scalar or vector with one element for each regressor')
if regularizeTheta is not None:
if callable(regularizeTheta):
## User can input their own function to regularize theta.
# Must input a cvxpy variable vector and output a scalar
# or a vector with one element for each parameter.
try:
regThetaObj = regularizeTheta(model['theta']) * beta
except:
raise ValueError('Check custom regularizer for model {}'.format(model['name']))
if regThetaObj.size[0]* regThetaObj.size[1] != 1:
raise ValueError('Check custom regularizer for model {}, make sure it returns a scalar'.format(model['name']))
elif regularizeTheta.lower() == 'l2':
## Sum square errors.
regThetaObj = cvp.norm(model['theta'] * beta)
elif regularizeTheta.lower() == 'l1':
regThetaObj = cvp.norm(model['theta'] * beta, 1)
elif regularizeTheta.lower() == 'diff_l2':
if numWind==1:
regThetaObj = 0
else:
if regressor.shape[1] == numWind: # this actually corresponds to the solar model (no intercept)
thetaDiffVec = cvp.diff(model['theta'])
else:
thetaDiffVec = cvp.vstack(cvp.diff(model['theta'][0:numWind]),cvp.diff(model['theta'][numWind:2*numWind]))
regThetaObj = cvp.norm(thetaDiffVec,2) * beta
elif regularizeTheta.lower() == 'diff_l1':
if numWind==1:
regThetaObj = 0
else:
if regressor.shape[1] == numWind: # this actually corresponds to the solar model (no intercept)
thetaDiffVec = cvp.diff(model['theta'])
else:
thetaDiffVec = cvp.vstack(cvp.diff(model['theta'][0:numWind]),cvp.diff(model['theta'][numWind:2*numWind]))
regThetaObj = cvp.norm(thetaDiffVec,1) * beta
else:
regThetaObj = 0
## Define cost function to regularize source signal ****************
# ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** *
# Check that gamma is scalar
gamma = np.array(gamma)
if gamma.size != 1:
raise NameError('Gamma must be scalar')
## Calculate regularization.
if regularizeSource is not None:
if idxScrReg is not None:
scrVec = model['source'][0:idxScrReg[0]]
idxStart = idxScrReg[0]+1
for idxEnd in idxScrReg[1:]:
scrCur = cvp.diff(model['source'][idxStart:idxEnd])
scrVec = cvp.vstack(scrVec,scrCur)
idxStart = idxEnd+1
scrVec = cvp.vstack(scrVec,cvp.diff(model['source'][idxStart:]))
else:
scrVec = cvp.diff(model['source'])
if callable(regularizeSource):
## User can input their own function to regularize the source signal.
# Must input a cvxpy variable vector and output a scalar.
regSourceObj = regularizeSource(scrVec) * gamma
elif regularizeSource.lower() == 'diff1_ss':
regSourceObj = cvp.sum_squares(scrVec) * gamma
elif regularizeSource.lower() == 'diff_l1':
regSourceObj = cvp.norm(scrVec,1) * gamma
elif regularizeSource.lower() == 'diff_l2':
regSourceObj = cvp.norm(scrVec,2) * gamma
else:
regSourceObj = 0
## Sum total model objective
model['obj'] = modelObj + regThetaObj + regSourceObj
## Append model to models list
self.models[name]= model
def __analyze_distribution(self, data, plot=None, figsize=(8, 6)):
# Calculate empirical CDF
x = np.sort(np.copy(data))
x = x[x > 0]
x = np.concatenate([[0.], x, [1.]])
y = np.linspace(0, 1, len(x))
# Resample the CDF to get an even spacing of points along the x-axis
f = interp1d(x, y)
x_rs = np.linspace(0, 1, 5000)
y_rs = f(x_rs)
# Fit statistical model to resampled CDF that has sparse 2nd order difference
y_hat = cvx.Variable(len(y_rs))
mu = cvx.Parameter(nonneg=True)
mu.value = 1e1
error = cvx.sum_squares(y_rs - y_hat)
reg = cvx.norm(cvx.diff(y_hat, k=2), p=1)
objective = cvx.Minimize(error + mu * reg)
constraints = [
y_rs[0] == y_hat[0],
y[-1] == y_hat[-1]
]
problem = cvx.Problem(objective, constraints)
problem.solve(solver='MOSEK')
# Look for outliers in the 2nd order difference to identify point masses from clipping
local_curv = cvx.diff(y_hat, k=2).value
ref_slope = cvx.diff(y_hat, k=1).value[:-1]
threshold = -0.5
# metric = local_curv / ref_slope
metric = np.min([
local_curv / ref_slope,
np.concatenate([
(local_curv[:-1] + local_curv[1:]) / ref_slope[:-1],
[local_curv[-1] / ref_slope[-1]]
]),
np.concatenate([
(local_curv[:-2] + local_curv[1:-1] + local_curv[2:]) / ref_slope[:-2],
[local_curv[-2:] / ref_slope[-2:]]
], axis=None)
], axis=0)
point_masses = np.concatenate(
[[False], np.logical_and(metric <= threshold, ref_slope > 3e-4), # looking for drops of more than 65%
[False]])
# Catch if the PDF ends in a point mass at the high value
if np.logical_or(cvx.diff(y_hat, k=1).value[-1] > 1e-3,
np.allclose(cvx.diff(y_hat, k=1).value[-1],
np.max(cvx.diff(y_hat, k=1).value))):
point_masses[-2] = True
# Reduce clusters of detected points to single points
pm_reduce = np.zeros_like(point_masses, dtype=np.bool)
for ix in range(len(point_masses) - 1):
if ~point_masses[ix] and point_masses[ix + 1]:
begin_cluster = ix + 1
elif point_masses[ix] and ~point_masses[ix + 1]:
end_cluster = ix
try:
ix_select = np.argmax(metric[begin_cluster:end_cluster + 1])
except ValueError:
pm_reduce[begin_cluster] = True
else:
pm_reduce[begin_cluster + ix_select] = True
point_masses = pm_reduce
point_mass_values = x_rs[point_masses]
if plot is None:
return point_mass_values
elif plot == 'pdf':
fig = plt.figure(figsize=figsize)
plt.hist(data[data > 0], bins=100, alpha=0.5, label='histogram')
scale = np.histogram(data[data > 0], bins=100)[0].max() \
/ cvx.diff(y_hat, k=1).value.max()
plt.plot(x_rs[:-1], scale * cvx.diff(y_hat, k=1).value,
color='orange', linewidth=1, label='piecewise constant PDF estimate')
for count, val in enumerate(point_mass_values):
if count == 0:
plt.axvline(val, linewidth=1, linestyle=':',
color='green', label='detected point mass')
else:
plt.axvline(val, linewidth=1, linestyle=':',
color='green')
return fig
elif plot == 'cdf':
fig = plt.figure(figsize=figsize)
plt.plot(x_rs, y_rs, linewidth=1, label='empirical CDF')
plt.plot(x_rs, y_hat.value, linewidth=3, color='orange', alpha=0.57,
label='estimated CDF')
if len(point_mass_values) > 0:
plt.scatter(x_rs[point_masses], y_rs[point_masses],
color='red', marker='o',
label='detected point mass')
return fig
elif plot == 'diffs':
fig, ax = plt.subplots(nrows=2, sharex=True, figsize=figsize)
y1 = cvx.diff(y_hat, k=1).value
y2 = metric
ax[0].plot(x_rs[:-1], y1)
ax[1].plot(x_rs[1:-1], y2)
ax[1].axhline(threshold, linewidth=1, color='r', ls=':',
label='decision boundary')
if len(point_mass_values) > 0:
ax[0].scatter(x_rs[point_masses],
y1[point_masses[1:]],
color='red', marker='o',
label='detected point mass')
ax[1].scatter(x_rs[point_masses],
y2[point_masses[1:-1]],
color='red', marker='o',
label='detected point mass')
ax[0].set_title('1st order difference of CDF fit')
ax[1].set_title('2nd order difference of CDF fit')
ax[1].legend()
plt.tight_layout()
return fig
def updateSourceObj(self, sourcename):
if sourcename.lower() == 'all':
for name in self.models.keys():
self.updateSourceObj(name)
else:
model = self.models[sourcename]
## Define objective function to fit model to regressors
## **CHANGE MT: I moved the alpha variable to be inside the norms so that
## it can be time varying. I'm adding a check above to ensure that alpha is
## a scalar or a vector of length N.
if model['costFunction'].lower() == 'sse':
residuals = (model['source'] - model['regressor'] * model['theta'])
modelObj = cvp.sum_squares( cvp.mul_elemwise( model['alpha'] ** .5 , residuals ) )
elif model['costFunction'].lower() == 'l1':
residuals = (model['source'] - model['regressor'] * model['theta'])
modelObj = cvp.norm( cvp.mul_elemwise( model['alpha'] , residuals ) ,1)
elif model['costFunction'].lower()=='l2':
residuals = (model['source'] - model['regressor'] * model['theta'])
modelObj = cvp.norm( cvp.mul_elemwise( model['alpha'] , residuals ) ,2)
else:
raise ValueError('{} wrong option, use "sse","l2" or "l1"'.format(costFunction))
## Define cost function to regularize theta ****************
# ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** *
# Check that beta is scalar or of length of number of parameters.
model['beta'] = np.array(model['beta'])
if model['beta'].size not in [1, model['order']]:
raise ValueError('Beta must be scalar or vector with one element for each regressor')
if model['regularizeTheta'] is not None:
if callable(model['regularizeTheta']):
## User can input their own function to regularize theta.
# Must input a cvxpy variable vector and output a scalar
# or a vector with one element for each parameter.
## TODO: TRY CATCH TO ENSURE regularizeTheta WORKS AND RETURNS SCALAR
try:
regThetaObj = model['regularizeTheta'](model['theta']) * model['beta']
except:
raise ValueError('Check custom regularizer for model {}'.format(model['name']))
if regThetaObj.size[0]* regThetaObj.size[1] != 1:
raise ValueError('Check custom regularizer for model {}, make sure it returns a scalar'.format(model['name']))
elif model['regularizeTheta'].lower() == 'l2':
## Sum square errors.
regThetaObj = cvp.norm(model['theta'] * model['beta'])
elif model['regularizeTheta'].lower() == 'l1':
regThetaObj = cvp.norm(model['theta'] * model['beta'], 1)
else:
regThetaObj = 0
## Define cost function to regularize source signal ****************
# ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** ***** *
# Check that gamma is scalar
model['gamma'] = np.array(model['gamma'])
if model['gamma'].size != 1:
raise NameError('Gamma must be scalar')
## Calculate regularization.
if model['regularizeSource'] is not None:
if callable(model['regularizeSource']):
## User can input their own function to regularize the source signal.
# Must input a cvxpy variable vector and output a scalar.
regSourceObj = model['regularizeSource'](model['source']) * model['gamma']
elif model['regularizeSource'].lower() == 'diff1_ss':
regSourceObj = cvp.sum_squares(cvp.diff(model['source'])) * model['gamma']
else:
raise Exception('regularizeSource must be a callable method, \`diff1_ss\`, or None')
else:
regSourceObj = 0
## Sum total model objective
model['obj'] = modelObj + regThetaObj + regSourceObj
## Append model to models list
self.models[sourcename] = model
