Soiling

Soiling Module

This module is for analyzing soiling trends in performance index (PI) data and daily energy data.

class solardatatools.algorithms.soiling.SoilingAnalysis(data_handler)

Bases: object

plot_analysis(figsize=None)

run(**config)

solardatatools.algorithms.soiling.soiling_seperation(observed, index_set=None, degradation_term=True, tau=0.85, w1=2, w2=0.03, w3=0.2, w4=500.0, iterations=5, solver=None, period=365, verbose=False)

Apply signal decomposition framework to Performance Index soiling estimation problem. The PI signal is a daily performance index, typically daily energy normalized by modeled or expected energy. PI signal assumed to contain components corresponding to

1. a soiling loss trend (sparse 1st-order differences)

2. a seasonal term (smooth, yearly periodic)

3. linear degradation

4. residual

Parameters:

• observed

• index_set

• degradation_term

• tau

• w1 – PWL weight - soiling term

• w2 – sparseness weight - soiling term

• w3 – asymmetric slopes - soiling term

• w4 – smoothness weight - seasonal term

• iterations

solardatatools.algorithms.soiling.soiling_seperation_old(observed, index_set=None, degradation_term=False, period=365, tau=0.85, w1=2, w2=0.01, w3=100, iterations=5, soiling_max=1.0, solver='MOSEK')

Apply signal decomposition framework to Performance Index soiling estimation problem. The PI signal is a daily performance index, typically daily energy normalized by modeled or expected energy. PI signal assumed to contain components corresponding to

1. a soiling loss trend (sparse 1st-order differences)

2. a seasonal term (smooth, yearly periodic)

3. linear degradation

4. residual

Parameters:

• observed

• index_set

• degradation_term

• tau

• w1 – PWL weight - soiling term

• w2 – sparseness weight - soiling term

• w3 – smoothness weight - seasonal term

• iterations