Added PV Fleets QA pipeline examples

docs/examples/pvfleets-qa-pipeline/README.rst

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+PVFleets QA Examples
+--------------------
+
+These examples highlight the QA processes for temperature, power and irradiance data streams that are used in the NREL
+PV Fleet Performance Data Initiative (https://www.nrel.gov/pv/fleet-performance-data-initiative.html).

docs/examples/pvfleets-qa-pipeline/pvfleets-irradiance-qa.py

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+"""
+PV Fleets QA Process: Irradiance
+================================
+
+PV Fleets Irradiance QA Pipeline
+"""
+
+# %%
+# The NREL PV Fleets Data Initiative uses PVAnalytics routines to assess the
+# quality of systems' PV data. In this example, the PV Fleets process for
+# assessing the data quality of an irradiance data stream is shown. This
+# example pipeline illustrates how several PVAnalytics functions can be used
+# in sequence to assess the quality of an irradiance data stream.
+
+import pandas as pd
+import pathlib
+from matplotlib import pyplot as plt
+import pvanalytics
+import pvlib
+from pvanalytics.quality import data_shifts as ds
+from pvanalytics.quality import gaps
+from pvanalytics.quality.outliers import zscore
+from pvanalytics.features.daytime import power_or_irradiance
+from pvanalytics.quality.time import shifts_ruptures
+from pvanalytics.features import daytime
+
+# %%
+# First, we import a POA irradiance data stream from a PV installation
+# at NREL. This data set is publicly available via the PVDAQ database in the
+# DOE Open Energy Data Initiative (OEDI)
+# (https://data.openei.org/submissions/4568), under system ID 15.
+# This data is timezone-localized.
+
+pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
+file = pvanalytics_dir / 'data' / 'system_15_poa_irradiance.parquet'
+time_series = pd.read_parquet(file)
+time_series.set_index('measured_on', inplace=True)
+time_series.index = pd.to_datetime(time_series.index)
+time_series = time_series['poa_irradiance__484']
+latitude = 39.7406
+longitude = -105.1775
+data_freq = '15min'
+time_series = time_series.asfreq(data_freq)
+
+# %%
+# First, let's visualize the original time series as reference.
+
+time_series.plot(title="Original Time Series")
+plt.xlabel("Date")
+plt.ylabel("Irradiance, W/m^2")
+plt.tight_layout()
+plt.show()
+
+# %%
+# Now, let's run basic data checks to identify stale and abnormal/outlier
+# data in the time series. Basic data checks include the following steps:
+#
+# 1) Flatlined/stale data periods
+#    (:py:func:`pvanalytics.quality.gaps.stale_values_round`)
+# 2) Negative irradiance data
+# 3) "Abnormal" data periods, which are defined as less than 10% of the
+#    daily time series mean OR greater than 1300
+# 4) Outliers, which are defined as more than one 4 standard deviations
+#    away from the mean (:py:func:`pvanalytics.quality.outliers.zscore`)
+
+# REMOVE STALE DATA (that isn't during nighttime periods)
+# Day/night mask
+daytime_mask = power_or_irradiance(time_series)
+# Stale data mask
+stale_data_mask = gaps.stale_values_round(time_series,
+                                          window=3,
+                                          decimals=2)
+stale_data_mask = stale_data_mask & daytime_mask
+
+# REMOVE NEGATIVE DATA
+negative_mask = (time_series < 0)
+
+# FIND ABNORMAL PERIODS
+daily_min = time_series.resample('D').min()
+erroneous_mask = (daily_min > 50)
+erroneous_mask = erroneous_mask.reindex(index=time_series.index,
+                                        method='ffill',
+                                        fill_value=False)
+
+# Remove values greater than or equal to 1300
+out_of_bounds_mask = (time_series >= 1300)
+
+# FIND OUTLIERS (Z-SCORE FILTER)
+zscore_outlier_mask = zscore(time_series,
+                             zmax=4,
+                             nan_policy='omit')
+
+# Get the percentage of data flagged for each issue, so it can later be logged
+pct_stale = round((len(time_series[
+    stale_data_mask].dropna())/len(time_series.dropna())*100), 1)
+pct_negative = round((len(time_series[
+    negative_mask].dropna())/len(time_series.dropna())*100), 1)
+pct_erroneous = round((len(time_series[
+    erroneous_mask].dropna())/len(time_series.dropna())*100), 1)
+pct_outlier = round((len(time_series[
+    zscore_outlier_mask].dropna())/len(time_series.dropna())*100), 1)
+
+# Visualize all of the time series issues (stale, abnormal, outlier, etc)
+time_series.plot()
+labels = ["Irradiance"]
+if any(stale_data_mask):
+    time_series.loc[stale_data_mask].plot(ls='', marker='o', color="green")
+    labels.append("Stale")
+if any(negative_mask):
+    time_series.loc[negative_mask].plot(ls='', marker='o', color="orange")
+    labels.append("Negative")
+if any(erroneous_mask):
+    time_series.loc[erroneous_mask].plot(ls='', marker='o', color="yellow")
+    labels.append("Abnormal")
+if any(out_of_bounds_mask):
+    time_series.loc[out_of_bounds_mask].plot(ls='', marker='o', color="yellow")
+    labels.append("Too High")
+if any(zscore_outlier_mask):
+    time_series.loc[zscore_outlier_mask].plot(
+        ls='', marker='o', color="purple")
+    labels.append("Outlier")
+plt.legend(labels=labels)
+plt.title("Time Series Labeled for Basic Issues")
+plt.xlabel("Date")
+plt.ylabel("Irradiance, W/m^2")
+plt.tight_layout()
+plt.show()
+
+
+# %%
+# Now, let's filter out any of the flagged data from the basic irradiance
+# checks (stale or abnormal data). Then we can re-visualize the data
+# post-filtering.
+
+# Filter the time series, taking out all of the issues
+issue_mask = ((~stale_data_mask) & (~negative_mask) & (~erroneous_mask) &
+              (~out_of_bounds_mask) & (~zscore_outlier_mask))
+time_series = time_series[issue_mask]
+time_series = time_series.asfreq(data_freq)
+
+# Visualize the time series post-filtering
+time_series.plot(title="Time Series Post-Basic Data Filtering")
+plt.xlabel("Date")
+plt.ylabel("Irradiance, W/m^2")
+plt.tight_layout()
+plt.show()
+
+# %%
+# We filter the time series based on its daily completeness score. This
+# filtering scheme requires at least 25% of data to be present for each day to
+# be included. We further require at least 10 consecutive days meeting this
+# 25% threshold to be included.
+
+# Visualize daily data completeness
+data_completeness_score = gaps.completeness_score(time_series)
+
+# Visualize data completeness score as a time series.
+data_completeness_score.plot()
+plt.xlabel("Date")
+plt.ylabel("Daily Completeness Score (Fractional)")
+plt.axhline(y=0.25, color='r', linestyle='-',
+            label='Daily Completeness Cutoff')
+plt.legend()
+plt.tight_layout()
+plt.show()
+
+# Trim the series based on daily completeness score
+trim_series = pvanalytics.quality.gaps.trim_incomplete(
+    time_series,
+    minimum_completeness=.25,
+    freq=data_freq)
+first_valid_date, last_valid_date = \
+    pvanalytics.quality.gaps.start_stop_dates(trim_series)
+time_series = time_series[first_valid_date.tz_convert(time_series.index.tz):
+                          last_valid_date.tz_convert(time_series.index.tz)]
+time_series = time_series.asfreq(data_freq)
+
+# %%
+# Next, we check the time series for any time shifts, which may be caused by
+# time drift or by incorrect time zone assignment. To do this, we compare
+# the modelled midday time for the particular system location to its
+# measured midday time. We use
+# :py:func:`pvanalytics.quality.gaps.stale_values_round`) to determine the
+# presence of time shifts in the series.
+
+# Get the modeled sunrise and sunset time series based on the system's
+# latitude-longitude coordinates
+modeled_sunrise_sunset_df = pvlib.solarposition.sun_rise_set_transit_spa(
+    time_series.index, latitude, longitude)
+
+# Calculate the midday point between sunrise and sunset for each day
+# in the modeled irradiance series
+modeled_midday_series = modeled_sunrise_sunset_df['sunrise'] + \
+    (modeled_sunrise_sunset_df['sunset'] -
+     modeled_sunrise_sunset_df['sunrise']) / 2
+
+# Run day-night mask on the irradiance time series
+daytime_mask = power_or_irradiance(time_series,
+                                   freq=data_freq,
+                                   low_value_threshold=.005)
+
+# Generate the sunrise, sunset, and halfway points for the data stream
+sunrise_series = daytime.get_sunrise(daytime_mask)
+sunset_series = daytime.get_sunset(daytime_mask)
+midday_series = sunrise_series + ((sunset_series - sunrise_series)/2)
+
+# Convert the midday and modeled midday series to daily values
+midday_series_daily, modeled_midday_series_daily = (
+    midday_series.resample('D').mean(),
+    modeled_midday_series.resample('D').mean())
+
+# Set midday value series as minutes since midnight, from midday datetime
+# values
+midday_series_daily = (midday_series_daily.dt.hour * 60 +
+                       midday_series_daily.dt.minute +
+                       midday_series_daily.dt.second / 60)
+modeled_midday_series_daily = \
+    (modeled_midday_series_daily.dt.hour * 60 +
+     modeled_midday_series_daily.dt.minute +
+     modeled_midday_series_daily.dt.second / 60)
+
+# Estimate the time shifts by comparing the modelled midday point to the
+# measured midday point.
+is_shifted, time_shift_series = shifts_ruptures(modeled_midday_series_daily,
+                                                midday_series_daily,
+                                                period_min=15,
+                                                shift_min=15,
+                                                zscore_cutoff=1.5)
+
+# Create a midday difference series between modeled and measured midday, to
+# visualize time shifts. First, resample each time series to daily frequency,
+# and compare the data stream's daily halfway point to the modeled halfway
+# point
+midday_diff_series = (modeled_midday_series.resample('D').mean() -
+                      midday_series.resample('D').mean()
+                      ).dt.total_seconds() / 60
+
+# Generate boolean for detected time shifts
+if any(time_shift_series != 0):
+    time_shifts_detected = True
+else:
+    time_shifts_detected = False
+
+# Build a list of time shifts for re-indexing. We choose to use dicts.
+time_shift_series.index = pd.to_datetime(
+    time_shift_series.index)
+changepoints = (time_shift_series != time_shift_series.shift(1))
+changepoints = changepoints[changepoints].index
+changepoint_amts = pd.Series(time_shift_series.loc[changepoints])
+time_shift_list = list()
+for idx in range(len(changepoint_amts)):
+    if idx < (len(changepoint_amts) - 1):
+        time_shift_list.append({"datetime_start":
+                                str(changepoint_amts.index[idx]),
+                                "datetime_end":
+                                    str(changepoint_amts.index[idx + 1]),
+                                "time_shift": changepoint_amts[idx]})
+    else:
+        time_shift_list.append({"datetime_start":
+                                str(changepoint_amts.index[idx]),
+                                "datetime_end":
+                                    str(time_shift_series.index.max()),
+                                "time_shift": changepoint_amts[idx]})
+
+# Correct any time shifts in the time series
+new_index = pd.Series(time_series.index, index=time_series.index)
+for i in time_shift_list:
+    new_index[(time_series.index >= pd.to_datetime(i['datetime_start'])) &
+              (time_series.index < pd.to_datetime(i['datetime_end']))] = \
+        time_series.index + pd.Timedelta(minutes=i['time_shift'])
+time_series.index = new_index
+
+# Remove duplicated indices and sort the time series (just in case)
+time_series = time_series[~time_series.index.duplicated(
+    keep='first')].sort_index()
+
+# Plot the difference between measured and modeled midday, as well as the
+# CPD-estimated time shift series.
+midday_diff_series.plot()
+time_shift_series.plot()
+plt.title("Midday Difference Time Shift Series")
+plt.xlabel("Date")
+plt.ylabel("Midday Difference (Modeled-Measured), Minutes")
+plt.tight_layout()
+plt.show()
+
+# Plot the heatmap of the irradiance time series
+plt.figure()
+# Get time of day from the associated datetime column
+time_of_day = pd.Series(time_series.index.hour +
+                        time_series.index.minute/60,
+                        index=time_series.index)
+# Pivot the dataframe
+dataframe = pd.DataFrame(pd.concat([time_series, time_of_day], axis=1))
+dataframe.columns = ["values", 'time_of_day']
+dataframe = dataframe.dropna()
+dataframe_pivoted = dataframe.pivot_table(index='time_of_day',
+                                          columns=dataframe.index.date,
+                                          values="values")
+plt.pcolormesh(dataframe_pivoted.columns,
+               dataframe_pivoted.index,
+               dataframe_pivoted,
+               shading='auto')
+plt.ylabel('Time of day [0-24]')
+plt.xlabel('Date')
+plt.xticks(rotation=60)
+plt.title('Post-Correction Heatmap, Time of Day')
+plt.colorbar()
+plt.tight_layout()
+plt.show()
+
+# %%
+# Next, we check the time series for any abrupt data shifts. We take the
+# longest continuous part of the time series that is free of data shifts.
+# We use :py:func:`pvanalytics.quality.data_shifts.detect_data_shifts` to
+# detect data shifts in the time series.
+
+# Resample the time series to daily mean
+time_series_daily = time_series.resample('D').mean()
+data_shift_start_date, data_shift_end_date = \
+    ds.get_longest_shift_segment_dates(time_series_daily)
+data_shift_period_length = (data_shift_end_date - data_shift_start_date).days
+
+# Get the number of shift dates
+data_shift_mask = ds.detect_data_shifts(time_series_daily)
+# Get the shift dates
+shift_dates = list(time_series_daily[data_shift_mask].index)
+if len(shift_dates) > 0:
+    shift_found = True
+else:
+    shift_found = False
+
+# Visualize the time shifts for the daily time series
+print("Shift Found:", shift_found)
+edges = [time_series_daily.index[0]] + \
+    shift_dates + [time_series_daily.index[-1]]
+fig, ax = plt.subplots()
+for (st, ed) in zip(edges[:-1], edges[1:]):
+    ax.plot(time_series_daily.loc[st:ed])
+plt.title("Daily Time Series Labeled for Data Shifts")
+plt.xlabel("Date")
+plt.ylabel("Mean Daily Irradiance (W/m^2)")
+plt.tight_layout()
+plt.show()
+
+# %%
+# We filter the time series to only include the longest
+# shift-free period.
+
+# Filter the time series to only include the longest shift-free period
+time_series = time_series[
+    (time_series.index >= data_shift_start_date.tz_convert(
+        time_series.index.tz)) &
+    (time_series.index <= data_shift_end_date.tz_convert(
+        time_series.index.tz))]
+
+time_series = time_series.asfreq(data_freq)
+
+
+# %%
+# Display the final irradiance time series, post-QA filtering.
+time_series.plot(title="Final Filtered Time Series")
+plt.xlabel("Date")
+plt.ylabel("AC Power (kW)")
+plt.tight_layout()
+plt.show()
+
+# %%
+# Generate a dictionary output for the QA assessment of this data stream,
+# including the percent stale and erroneous data detected, any shift dates,
+# and any detected time shifts.
+
+qa_check_dict = {"original_time_zone_offset": time_series.index.tz,
+                 "pct_stale": pct_stale,
+                 "pct_negative": pct_negative,
+                 "pct_erroneous": pct_erroneous,
+                 "pct_outlier": pct_outlier,
+                 "time_shifts_detected": time_shifts_detected,
+                 "time_shift_list": time_shift_list,
+                 "data_shifts": shift_found,
+                 "shift_dates": shift_dates}
+
+print("QA Results:")
+print(qa_check_dict)