Copyright (c) 2019, ETH Zurich, Computer Engineering Group
All rights reserved.
This work is licensed under the Creative Commons Attribution 4.0 International License.
To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or
send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
The file is part of the dataset entiled "Long-Term Tracing of Indoor Solar Harvesting" which complements the following publication:
L. Sigrist, A. Gomez, and L. Thiele. "Dataset: Tracing Indoor Solar Harvesting." In Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA '19), 2019. [under submission]
The dataset is published and documented on Zenodo:
Description
This Jupyter notebook imports the processed HDF5 dataset of the Indoor Solar Harvesting Dataset stored in the processed/
folder. After calculating additional power and energy data columns, the data is then aggregated in 5 min intervals.
Columns-wise statistics is calculated and a joint preview of the extracted power and illuminace is plotted.
In the last part the short-term predicion performance of a few energy prediction algorithms is compared on the presented dataset.
Python package requirements
The following Python packages are required to run this script: matplotlib numpy pandas tables
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas.plotting import register_matplotlib_converters
DATASET_PATH = './processed'
DATASET_POSITIONS = ['pos06', 'pos13', 'pos14', 'pos16', 'pos17', 'pos18']
DATASET_TIME_REFERENCE = 'relative' # see raw data import script for more explaination
# load dataset for the first position
position = DATASET_POSITIONS[4]
power_df = pd.read_hdf(os.path.join(DATASET_PATH, '{:s}_power_{:s}.h5'.format(position, DATASET_TIME_REFERENCE)), 'dataset')
sensor_df = pd.read_hdf(os.path.join(DATASET_PATH, '{:s}_sensor_{:s}.h5'.format(position, DATASET_TIME_REFERENCE)), 'dataset')
# alternative: load from pickle files
# power_df = pd.read_pickle(os.path.join(DATASET_PATH, '{:s}_power_{:s}.p.bz2'.format(position, DATASET_TIME_REFERENCE)))
# sensor_df = pd.read_pickle(os.path.join(DATAFRAME_BACKUP_PATH, '{:s}_sensor_{:s}.p.bz2'.format(position, DATASET_TIME_REFERENCE)))
# explicitly register matplotlib time converters for pandas
register_matplotlib_converters()
# derive energy and average sensor columns
power_df['P_in'] = -1 * power_df.I_in * power_df.V_in
power_df['P_bat'] = power_df.I_bat * power_df.V_bat
power_df['E_in'] = power_df.dt * power_df.P_in
power_df['E_bat'] = power_df.dt * power_df.P_bat
sensor_df['Ev'] = sensor_df[['Ev_left', 'Ev_right']].mean(1)
# power dataframe info
power_df.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 62649233 entries, 2017-07-27 13:28:26.523104 to 2019-08-01 07:31:36.045644 Data columns (total 9 columns): I_bat float64 I_in float64 V_bat float64 V_in float64 dt float64 P_in float64 P_bat float64 E_in float64 E_bat float64 dtypes: float64(9) memory usage: 4.7 GB
# sensor dataframe info
sensor_df.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 62649203 entries, 2017-07-27 13:28:26.524197 to 2019-08-01 07:31:36.046178 Data columns (total 7 columns): Ev_left float64 Ev_right float64 P_amb float64 RH_amb float64 T_amb float64 dt float64 Ev float64 dtypes: float64(7) memory usage: 3.7 GB
AGGREGATE_WINDOW = '5min'
# aggregate power data (see also: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)
power_agg_mean = power_df[['I_bat', 'I_in', 'V_bat', 'V_in', 'P_in', 'P_bat']].resample(AGGREGATE_WINDOW).mean()
power_agg_sum = power_df[['E_in', 'E_bat']].resample(AGGREGATE_WINDOW).sum()
power_agg_count = power_df['dt'].resample(AGGREGATE_WINDOW).count()
power_agg = pd.concat([power_agg_mean, power_agg_sum, power_agg_count.rename('count')])
# aggregate sensor data (see also: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)
sensor_agg_mean = sensor_df[['Ev_left', 'Ev_right', 'P_amb', 'RH_amb', 'T_amb', 'Ev']].resample(AGGREGATE_WINDOW).mean()
sensor_agg_count = sensor_df['dt'].resample(AGGREGATE_WINDOW).count()
sensor_agg = pd.concat([sensor_agg_mean, sensor_agg_count.rename('count')])
# summary of aggregated power data
power_agg.describe()
E_bat | E_in | I_bat | I_in | P_bat | P_in | V_bat | V_in | 0 | |
---|---|---|---|---|---|---|---|---|---|
count | 211610.000000 | 2.116100e+05 | 2.095740e+05 | 2.095740e+05 | 209574.000000 | 2.095740e+05 | 209574.000000 | 209574.000000 | 211610.000000 |
mean | 0.007010 | 9.972268e-03 | 5.611414e-06 | -2.318991e-05 | 0.000024 | 3.356530e-05 | 4.204692 | 0.502369 | 296.059846 |
std | 0.021298 | 2.703107e-02 | 1.695784e-05 | 4.542448e-05 | 0.000071 | 9.048706e-05 | 0.000450 | 0.604887 | 29.223198 |
min | -0.000851 | -3.032250e-07 | -6.742763e-07 | -1.544100e-03 | -0.000003 | -1.010573e-09 | 4.203416 | 0.001166 | 0.000000 |
25% | -0.000652 | 2.510009e-10 | -5.165900e-07 | -3.618361e-05 | -0.000002 | 9.253930e-13 | 4.204360 | 0.003062 | 299.000000 |
50% | -0.000642 | 7.302863e-05 | -5.094917e-07 | -3.586549e-06 | -0.000002 | 3.349444e-07 | 4.204629 | 0.087342 | 299.000000 |
75% | 0.008108 | 1.070216e-02 | 6.590437e-06 | -2.791940e-10 | 0.000028 | 3.649276e-05 | 4.205019 | 1.009730 | 299.000000 |
max | 0.953677 | 1.073161e+00 | 7.559951e-04 | 5.175565e-09 | 0.003178 | 3.576578e-03 | 4.206014 | 2.318222 | 396.000000 |
# summary of aggregated sensor data
sensor_agg.describe()
0 | Ev | Ev_left | Ev_right | P_amb | RH_amb | T_amb | |
---|---|---|---|---|---|---|---|
count | 211610.000000 | 209574.000000 | 209574.000000 | 209574.000000 | 209574.000000 | 209574.000000 | 209574.000000 |
mean | 296.059704 | 193.762934 | 187.843502 | 199.682365 | 96144.165067 | 0.338915 | 25.088373 |
std | 29.223382 | 312.935361 | 303.776361 | 322.751672 | 771.230698 | 0.094220 | 1.555676 |
min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 92658.317355 | 0.136605 | 20.215843 |
25% | 299.000000 | 0.000000 | 0.000000 | 0.000000 | 95761.815620 | 0.264464 | 23.773026 |
50% | 299.000000 | 20.518395 | 20.180602 | 20.862416 | 96190.150457 | 0.320064 | 25.108639 |
75% | 299.000000 | 313.132107 | 303.806856 | 322.173913 | 96581.741867 | 0.406244 | 26.158611 |
max | 396.000000 | 4958.202341 | 5585.712375 | 5287.926421 | 98110.797475 | 0.654899 | 31.160602 |
[f, ax] = plt.subplots(2, 1, figsize=(12, 6), sharex=True)
_ = f.autofmt_xdate()
p1 = ax[0].plot(power_agg.index, 1e6 * power_agg[['P_in']], alpha=0.75, label='{:s} - P_in'.format(position))
p2 = ax[0].plot(power_agg.index, 1e6 * power_agg[['P_bat']], alpha=0.75, label='{:s} - P_bat'.format(position))
_ = ax[0].set(
ylabel='Power [uW]',
xlim=[power_agg.index.min(), power_agg.index.max()],
)
_ = ax[0].legend(loc='upper right')
p3 = ax[1].plot(sensor_agg.index, sensor_agg[['Ev_left']], alpha=0.75, label='{:s} - Ev_left'.format(position))
p4 = ax[1].plot(sensor_agg.index, sensor_agg[['Ev_right']], alpha=0.75, label='{:s} - Ev_right'.format(position))
_ = ax[1].set(
# xlabel='time',
ylabel='Illuminance [lux]',
)
_ = ax[1].legend(loc='upper right')
plt.show()
predict_reference = 1e6 * power_agg['P_bat']
predict = pd.DataFrame()
MA_WINDOW = 10
EWMA_SPAN = 10
# conservative predictor (predict currently observed value)
predict_cons = predict_reference.shift(1)
predict['CONS_abs'] = np.abs(predict_cons - predict_reference)
predict['CONS_rel'] = 100 * predict['CONS_abs'] / np.abs(predict_reference)
# moving average predictor
predict_ma = predict_reference.rolling(window=MA_WINDOW).mean()
predict['MA_abs'] = np.abs(predict_ma - predict_reference)
predict['MA_rel'] = 100 * predict['MA_abs'] / np.abs(predict_reference)
# exponentially weighted moving average predictor
predict_ewma = predict_reference.ewm(span=EWMA_SPAN).mean()
predict['EWMA_abs'] = np.abs(predict_ewma - predict_reference)
predict['EWMA_rel'] = 100 * predict['EWMA_abs'] / np.abs(predict_reference)
# perform statistics only for power levels higher than 10% of their average to reduce error amplification for very low values
stat_filter = predict_reference >= 0.1 * predict_reference.mean()
print(stat_filter.sum())
80508
predict.loc[stat_filter].describe()
CONS_abs | CONS_rel | MA_abs | MA_rel | EWMA_abs | EWMA_rel | |
---|---|---|---|---|---|---|
count | 80501.000000 | 80501.000000 | 80445.000000 | 80445.000000 | 80508.000000 | 80508.000000 |
mean | 14.365786 | 27.940112 | 23.168240 | 64.731219 | 19.596917 | 55.342108 |
std | 53.841365 | 132.288838 | 56.664336 | 191.171364 | 48.245529 | 153.927068 |
min | 0.000010 | 0.000104 | 0.000965 | 0.001804 | 0.000000 | 0.000000 |
25% | 1.410656 | 4.122457 | 4.545701 | 12.794962 | 4.037618 | 11.044705 |
50% | 3.327387 | 11.337537 | 9.570142 | 27.510205 | 8.487239 | 23.826817 |
75% | 10.241293 | 28.023543 | 21.126374 | 55.481711 | 17.835774 | 47.859848 |
max | 2787.129012 | 16556.191973 | 2747.075552 | 11923.164317 | 2452.248665 | 8495.643196 |
f = plt.figure(figsize=(7, 4))
ax, lns = predict[stat_filter].boxplot(column=['CONS_abs', 'MA_abs', 'EWMA_abs'], whis=[10,90], showfliers=False, return_type='both')
_ = ax.set(
xticklabels=['CONS', 'MA', 'EWMA'],
ylabel='Absolute Error [uW]',
title='Absolute Predictor Errors ({:s})'.format(position),
)
for l in lns['whiskers'] + lns['caps'] + lns['boxes'] + lns['medians']:
_ = l.set(linewidth=2)
f = plt.figure(figsize=(7,4))
ax, lns = predict[stat_filter].boxplot(column=['CONS_rel', 'MA_rel', 'EWMA_rel'], whis=[10,90], showfliers=False, return_type='both')
_ = ax.set(
xticklabels=['CONS', 'MA', 'EWMA'],
ylabel='Relative Error [%]',
title='Relative Predictor Errors ({:s})'.format(position),
)
for l in lns['whiskers'] + lns['caps'] + lns['boxes'] + lns['medians']:
_ = l.set(linewidth=2)
for position in DATASET_POSITIONS:
print('*** Power Measurement Statistics for `{:s}`:'.format(position))
## load power data
power_df = pd.read_hdf(os.path.join(DATASET_PATH, '{:s}_power_{:s}.h5'.format(position, DATASET_TIME_REFERENCE)), 'dataset')
# derive energy and average sensor columns
power_df['P_in'] = -1 * power_df.I_in * power_df.V_in
power_df['P_bat'] = power_df.I_bat * power_df.V_bat
power_df['E_in'] = power_df.dt * power_df.P_in
power_df['E_bat'] = power_df.dt * power_df.P_bat
# aggregate power data
power_daily = pd.concat([
power_df[['I_bat', 'I_in', 'V_bat', 'V_in', 'P_in', 'P_bat']].resample('1d').mean(),
power_df[['E_in', 'E_bat']].resample('1d').sum(),
],
sort=True
)
power_daily_deviation = np.abs(power_daily - power_daily.mean())
# print daily power statistics
print('>> Aggregate daily:\n', power_daily.describe())
print('>> Aggregate absolut deviation stats:\n', power_daily_deviation.describe())
print('*** Sensor Measurement Statistics for `{:s}`:'.format(position))
## load power data
sensor_df = pd.read_hdf(os.path.join(DATASET_PATH, '{:s}_sensor_{:s}.h5'.format(position, DATASET_TIME_REFERENCE)), 'dataset')
# derive energy and average sensor columns
sensor_df['Ev'] = sensor_df[['Ev_left', 'Ev_right']].mean(1)
# aggregate power data
sensor_daily = sensor_df[['Ev_left', 'Ev_right', 'P_amb', 'RH_amb', 'T_amb', 'Ev']].resample('1d').mean()
power_daily_deviation = np.abs(sensor_daily - sensor_daily.mean())
# print daily power statistics
print('>> Aggregate daily:\n', sensor_daily.describe())
print('>> Aggregate absolut deviation stats:\n', power_daily_deviation.describe())
*** Power Measurement Statistics for `pos06`: >> Aggregate daily: E_bat E_in I_bat I_in P_bat \ count 691.000000 691.000000 6.890000e+02 6.890000e+02 689.000000 mean 1.608144 2.020564 4.465129e-06 -1.216289e-05 0.000019 std 1.133325 1.252914 3.133576e-06 7.009484e-06 0.000013 min -0.200063 0.000000 -6.300470e-07 -2.756504e-05 -0.000003 25% 0.313648 0.606863 9.001523e-07 -1.749609e-05 0.000004 50% 1.915928 2.358828 5.286263e-06 -1.393867e-05 0.000022 75% 2.482491 2.979397 6.887221e-06 -4.372090e-06 0.000029 max 4.106607 4.781431 1.131487e-05 -1.357160e-07 0.000048 P_in V_bat V_in count 6.890000e+02 689.000000 689.000000 mean 2.357148e-05 4.200779 0.802320 std 1.452489e-05 0.000125 0.243503 min 8.657343e-09 4.200420 0.063839 25% 7.197961e-06 4.200694 0.694686 50% 2.734108e-05 4.200778 0.851609 75% 3.476172e-05 4.200872 0.944213 max 5.534060e-05 4.201148 1.419289 >> Aggregate absolut deviation stats: E_bat E_in I_bat I_in P_bat \ count 691.000000 691.000000 6.890000e+02 6.890000e+02 6.890000e+02 mean 0.988678 1.091897 2.722940e-06 6.067205e-06 1.143836e-05 std 0.552742 0.613047 1.547296e-06 3.502626e-06 6.499713e-06 min 0.005557 0.002839 6.772807e-10 4.336127e-08 3.077378e-09 25% 0.515913 0.571622 1.390772e-06 3.055126e-06 5.841861e-06 50% 0.970525 1.069250 2.680754e-06 5.880660e-06 1.126106e-05 75% 1.484452 1.637926 4.116189e-06 9.075655e-06 1.729101e-05 max 2.498463 2.760868 6.849738e-06 1.540215e-05 2.877328e-05 P_in V_bat V_in count 6.890000e+02 6.890000e+02 689.000000 mean 1.260877e-05 1.020040e-04 0.183788 std 7.194475e-06 7.269653e-05 0.159583 min 3.742360e-08 3.735013e-09 0.000717 25% 6.435539e-06 4.324173e-05 0.066671 50% 1.235664e-05 8.814601e-05 0.136949 75% 1.901343e-05 1.496145e-04 0.250287 max 3.176912e-05 3.687154e-04 0.738481 *** Sensor Measurement Statistics for `pos06`: >> Aggregate daily: Ev_left Ev_right P_amb RH_amb T_amb \ count 691.000000 691.000000 691.000000 691.000000 691.000000 mean 51.754776 50.467554 96093.061407 0.334256 24.377751 std 28.972877 30.148739 760.463705 0.101193 0.758234 min 4.000000 0.000000 93192.590154 0.118216 22.452062 25% 18.017916 15.835627 95714.402033 0.253137 23.919683 50% 59.808716 58.790562 96120.540793 0.311985 24.377606 75% 73.723951 73.212795 96536.504271 0.410064 24.876390 max 112.854583 113.547278 97964.876428 0.591840 26.480823 Ev count 691.000000 mean 51.111165 std 29.559227 min 2.000000 25% 16.950292 50% 59.303382 75% 73.519824 max 113.172608 >> Aggregate absolut deviation stats: Ev_left Ev_right P_amb RH_amb T_amb Ev count 691.000000 691.000000 691.000000 691.000000 691.000000 691.000000 mean 25.241793 26.275070 565.151364 0.085930 0.596723 25.757877 std 14.189999 14.750139 508.375934 0.053342 0.467252 14.467831 min 0.194397 0.117460 0.504572 0.000138 0.000145 0.038468 25% 13.053382 13.839721 178.097169 0.044526 0.221573 13.393443 50% 24.945097 25.961775 408.743071 0.079932 0.466982 25.555664 75% 37.485667 38.889079 828.558342 0.116534 0.887347 38.101557 max 61.099806 63.079724 2900.471254 0.257585 2.103072 62.061442 *** Power Measurement Statistics for `pos13`: >> Aggregate daily: E_bat E_in I_bat I_in P_bat \ count 736.000000 736.000000 7.250000e+02 7.250000e+02 725.000000 mean 1.185071 1.572258 3.339765e-06 -1.053901e-05 0.000014 std 0.871043 0.985920 2.418283e-06 5.992946e-06 0.000010 min -0.199537 0.000000 -5.502723e-07 -2.932941e-05 -0.000002 25% 0.315625 0.628191 1.084560e-06 -1.493810e-05 0.000005 50% 1.312110 1.728839 3.703299e-06 -1.142684e-05 0.000016 75% 1.830408 2.310709 5.077210e-06 -4.989323e-06 0.000021 max 3.496292 4.192830 1.102722e-05 -1.006350e-07 0.000046 P_in V_bat V_in count 7.250000e+02 725.000000 725.000000 mean 1.859790e-05 4.196591 0.793358 std 1.140911e-05 0.000243 0.276216 min 5.372375e-09 4.196061 0.052754 25% 8.078294e-06 4.196391 0.617121 50% 2.037442e-05 4.196589 0.829368 75% 2.681999e-05 4.196786 0.984523 max 5.452613e-05 4.197199 1.628648 >> Aggregate absolut deviation stats: E_bat E_in I_bat I_in P_bat \ count 736.000000 736.000000 7.250000e+02 7.250000e+02 7.250000e+02 mean 0.745324 0.842402 2.045381e-06 5.053223e-06 8.583337e-06 std 0.449947 0.511304 1.287917e-06 3.216376e-06 5.404640e-06 min 0.000990 0.001077 1.247142e-08 9.946301e-10 5.305718e-08 25% 0.347580 0.403954 9.136128e-07 2.225500e-06 3.834900e-06 50% 0.733491 0.824414 1.967095e-06 4.835571e-06 8.255122e-06 75% 1.144898 1.282011 3.138810e-06 7.683687e-06 1.317195e-05 max 2.311221 2.620572 7.687458e-06 1.879040e-05 3.225788e-05 P_in V_bat V_in count 7.250000e+02 7.250000e+02 725.000000 mean 9.644538e-06 2.061145e-04 0.221163 std 6.084594e-06 1.294176e-04 0.165272 min 5.807756e-09 4.219153e-07 0.000962 25% 4.331611e-06 9.746821e-05 0.090564 50% 9.299484e-06 1.971974e-04 0.187877 75% 1.477251e-05 2.930126e-04 0.316603 max 3.592822e-05 6.082589e-04 0.835290 *** Sensor Measurement Statistics for `pos13`: >> Aggregate daily: Ev_left Ev_right P_amb RH_amb T_amb \ count 725.000000 725.000000 725.000000 725.000000 725.000000 mean 48.360728 49.050090 96072.818296 0.336363 24.696032 std 29.351162 29.094558 749.188916 0.114248 1.003387 min 0.000000 0.000000 93160.071993 0.097719 22.130072 25% 19.098308 21.120144 95710.559490 0.243295 23.913744 50% 53.857533 53.918916 96105.150809 0.314958 24.690927 75% 70.689025 71.451357 96505.528299 0.425149 25.484453 max 132.760905 136.771607 97934.807035 0.650913 27.407403 Ev count 725.000000 mean 48.705409 std 29.217054 min 0.000000 25% 20.020843 50% 53.842044 75% 70.772193 max 134.766256 >> Aggregate absolut deviation stats: Ev_left Ev_right P_amb RH_amb T_amb Ev count 725.000000 725.000000 725.000000 725.000000 725.000000 725.000000 mean 24.995056 24.673912 554.881438 0.096543 0.836467 24.829118 std 15.358222 15.389948 502.956613 0.060984 0.553301 15.372040 min 0.046115 0.129384 0.363627 0.000405 0.001166 0.047866 25% 11.690249 11.171416 166.618977 0.051190 0.364106 11.430020 50% 24.194831 23.941385 403.991035 0.091931 0.786929 24.123970 75% 38.528318 37.887352 789.892744 0.130832 1.248926 38.276155 max 84.400177 87.721517 2912.746302 0.314550 2.711371 86.060847 *** Power Measurement Statistics for `pos14`: >> Aggregate daily: E_bat E_in I_bat I_in P_bat \ count 736.000000 736.000000 7.320000e+02 7.320000e+02 732.000000 mean 14.931738 14.183981 4.151121e-05 -7.673425e-05 0.000174 std 11.625900 11.356970 3.198009e-05 5.771281e-05 0.000134 min -0.031708 0.000000 -4.940698e-07 -3.941358e-04 -0.000002 25% 5.788332 5.820560 1.658916e-05 -1.098605e-04 0.000070 50% 12.781081 11.391625 3.536249e-05 -6.358929e-05 0.000148 75% 21.524596 20.721798 5.979790e-05 -3.392347e-05 0.000251 max 74.921864 78.995273 2.065978e-04 -4.263754e-08 0.000867 P_in V_bat V_in count 7.320000e+02 732.000000 732.000000 mean 1.655107e-04 4.197223 1.047120 std 1.311595e-04 0.000180 0.231225 min 2.492477e-09 4.196680 0.058623 25% 6.922681e-05 4.197120 0.851693 50% 1.327957e-04 4.197229 1.076660 75% 2.407005e-04 4.197353 1.249132 max 9.142965e-04 4.197622 1.436656 >> Aggregate absolut deviation stats: E_bat E_in I_bat I_in P_bat \ count 736.000000 736.000000 7.320000e+02 7.320000e+02 7.320000e+02 mean 9.096285 8.620093 2.501155e-05 4.409984e-05 1.049808e-04 std 7.232327 7.387399 1.990710e-05 3.719290e-05 8.355608e-05 min 0.030293 0.014660 6.682149e-08 9.230593e-09 2.838534e-07 25% 4.010170 3.980406 1.085044e-05 1.958066e-05 4.551945e-05 50% 8.246823 7.647307 2.263735e-05 3.869032e-05 9.502333e-05 75% 12.757668 11.666000 3.510314e-05 6.053267e-05 1.473379e-04 max 59.990125 64.811293 1.650866e-04 3.174016e-04 6.929154e-04 P_in V_bat V_in count 7.320000e+02 7.320000e+02 732.000000 mean 9.957607e-05 1.417082e-04 0.196699 std 8.528695e-05 1.104458e-04 0.121332 min 4.054419e-08 3.903869e-07 0.000155 25% 4.634705e-05 5.387712e-05 0.090091 50% 8.805051e-05 1.134680e-04 0.201648 75% 1.337715e-04 2.099569e-04 0.286374 max 7.487858e-04 5.438809e-04 0.988497 *** Sensor Measurement Statistics for `pos14`: >> Aggregate daily: Ev_left Ev_right P_amb RH_amb T_amb \ count 732.000000 732.000000 732.000000 732.000000 732.000000 mean 219.516501 273.067414 96143.099511 0.317223 24.888214 std 166.865110 211.683747 743.962472 0.099009 1.207530 min 0.000000 0.000000 93241.198898 0.085601 22.509971 25% 95.835653 119.368698 95791.299306 0.236664 23.946838 50% 176.096815 225.060833 96167.669412 0.306761 24.905447 75% 318.569567 384.267901 96569.167428 0.395263 25.606110 max 1205.926234 1407.569764 98009.849093 0.564860 28.460974 Ev count 732.000000 mean 246.291958 std 188.429671 min 0.000000 25% 107.236624 50% 200.503522 75% 352.629983 max 1306.747999 >> Aggregate absolut deviation stats: Ev_left Ev_right P_amb RH_amb T_amb \ count 732.000000 732.000000 732.000000 732.000000 732.000000 mean 129.205523 159.747634 548.434993 0.084411 0.971867 std 105.484882 138.765241 502.282543 0.051650 0.715758 min 0.075749 0.018710 0.779208 0.000096 0.000676 25% 59.660133 71.894446 157.671326 0.042883 0.414416 50% 117.923873 139.083063 395.265239 0.079634 0.815543 75% 175.377860 216.737483 788.252568 0.118938 1.488980 max 986.409733 1134.502351 2901.900613 0.247637 3.572760 Ev count 732.000000 mean 144.322231 std 121.030331 min 0.701271 25% 65.327310 50% 128.397903 75% 195.658752 max 1060.456042 *** Power Measurement Statistics for `pos16`: >> Aggregate daily: E_bat E_in I_bat I_in P_bat \ count 45.000000 45.000000 4.500000e+01 4.500000e+01 45.000000 mean 6.100844 7.073818 1.688764e-05 -3.980853e-05 0.000071 std 1.832897 2.144020 4.848516e-06 1.081692e-05 0.000020 min -0.036556 0.001565 -5.261171e-07 -6.042417e-05 -0.000002 25% 5.465758 6.284872 1.506048e-05 -4.643117e-05 0.000063 50% 6.031378 6.900763 1.661869e-05 -3.884907e-05 0.000070 75% 7.203094 8.402770 1.984793e-05 -3.588876e-05 0.000083 max 9.554936 11.423602 2.632647e-05 -2.645306e-07 0.000111 P_in V_bat V_in count 4.500000e+01 45.000000 45.000000 mean 8.232020e-05 4.200473 1.262978 std 2.362374e-05 0.000080 0.200845 min 9.487324e-08 4.200323 0.090625 25% 7.274236e-05 4.200429 1.257325 50% 7.986988e-05 4.200460 1.301528 75% 9.725534e-05 4.200533 1.342060 max 1.322175e-04 4.200721 1.409358 >> Aggregate absolut deviation stats: E_bat E_in I_bat I_in P_bat \ count 45.000000 45.000000 4.500000e+01 4.500000e+01 4.500000e+01 mean 1.257994 1.484432 3.394177e-06 7.528432e-06 1.425713e-05 std 1.319468 1.530753 3.424302e-06 7.683771e-06 1.438401e-05 min 0.034228 0.142053 1.719668e-07 8.057785e-07 7.208593e-07 25% 0.505720 0.609629 1.435571e-06 3.193899e-06 6.027425e-06 50% 0.887322 1.016476 2.440080e-06 5.201227e-06 1.025077e-05 75% 1.248042 1.497673 3.361818e-06 7.780998e-06 1.412121e-05 max 6.137400 7.072253 1.741376e-05 3.954399e-05 7.314635e-05 P_in V_bat V_in count 45.000000 45.000000 45.000000 mean 0.000017 0.000064 0.088453 std 0.000016 0.000047 0.179825 min 0.000001 0.000003 0.005653 25% 0.000007 0.000030 0.020295 50% 0.000012 0.000060 0.049924 75% 0.000017 0.000085 0.085103 max 0.000082 0.000248 1.172354 *** Sensor Measurement Statistics for `pos16`: >> Aggregate daily: Ev_left Ev_right P_amb RH_amb T_amb Ev count 45.000000 45.000000 45.000000 45.000000 45.000000 45.000000 mean 107.721711 94.280445 96115.484630 0.483360 25.544061 101.001078 std 26.870998 22.969144 365.516214 0.063060 0.782251 24.732756 min 3.210437 0.171481 94977.383708 0.352225 22.113771 1.690959 25% 100.183038 90.254945 95948.776871 0.433849 25.158707 95.700297 50% 107.467345 96.832700 96122.805764 0.482992 25.630881 101.561972 75% 121.297676 104.119679 96328.892220 0.529787 26.046665 113.046465 max 164.500552 129.115765 96791.568528 0.633938 26.802049 140.500598 >> Aggregate absolut deviation stats: Ev_left Ev_right P_amb RH_amb T_amb Ev count 45.000000 45.000000 45.000000 45.000000 45.000000 45.000000 mean 16.566242 13.727721 264.308711 0.050464 0.543062 14.844778 std 21.008876 18.298861 249.309649 0.037041 0.557044 19.655366 min 0.163747 0.555740 4.665499 0.000368 0.001532 0.394133 25% 4.200981 3.814738 100.127576 0.021977 0.212247 4.221684 50% 10.151111 7.674083 200.850844 0.048476 0.496801 7.253786 75% 17.788206 15.354188 348.122205 0.065477 0.700224 16.979014 max 104.511274 94.108965 1138.100922 0.150578 3.430290 99.310119 *** Power Measurement Statistics for `pos17`: >> Aggregate daily: E_bat E_in I_bat I_in P_bat \ count 736.000000 736.000000 7.320000e+02 7.320000e+02 732.000000 mean 2.015411 2.867163 5.607883e-06 -2.316164e-05 0.000024 std 2.123783 2.779118 5.888231e-06 1.619362e-05 0.000025 min -0.163350 0.000000 -5.166205e-07 -1.079698e-04 -0.000002 25% 0.704408 1.105505 2.004593e-06 -2.940479e-05 0.000008 50% 1.719575 2.506905 4.765195e-06 -2.254147e-05 0.000020 75% 2.610877 3.586470 7.206673e-06 -1.240811e-05 0.000030 max 14.238141 19.022680 3.919001e-05 -4.561762e-11 0.000165 P_in V_bat V_in count 7.320000e+02 732.000000 732.000000 mean 3.353499e-05 4.204692 0.501416 std 3.233973e-05 0.000435 0.204483 min 2.039127e-13 4.203887 0.002719 25% 1.308908e-05 4.204353 0.333064 50% 2.915337e-05 4.204611 0.516836 75% 4.167272e-05 4.205031 0.665341 max 2.201698e-04 4.205757 1.004148 >> Aggregate absolut deviation stats: E_bat E_in I_bat I_in P_bat \ count 736.000000 736.000000 7.320000e+02 7.320000e+02 7.320000e+02 mean 1.314064 1.709251 3.637744e-06 1.084600e-05 1.529564e-05 std 1.667735 2.190430 4.628172e-06 1.201818e-05 1.946197e-05 min 0.006081 0.001629 1.460051e-09 4.076126e-09 9.804354e-09 25% 0.403161 0.536215 1.115727e-06 3.377906e-06 4.693467e-06 50% 0.898777 1.119386 2.462498e-06 8.123341e-06 1.035385e-05 75% 1.800319 2.364246 4.900535e-06 1.515316e-05 2.060527e-05 max 12.222731 16.155518 3.358212e-05 8.480811e-05 1.412138e-04 P_in V_bat V_in count 7.320000e+02 732.000000 732.000000 mean 1.986191e-05 0.000364 0.170919 std 2.551124e-05 0.000237 0.112073 min 5.331526e-08 0.000006 0.000333 25% 6.229989e-06 0.000173 0.071412 50% 1.287596e-05 0.000339 0.166723 75% 2.708555e-05 0.000517 0.250401 max 1.866348e-04 0.001064 0.502732 *** Sensor Measurement Statistics for `pos17`: >> Aggregate daily: Ev_left Ev_right P_amb RH_amb T_amb \ count 732.000000 732.000000 732.000000 732.000000 732.000000 mean 187.534971 199.351722 96144.364282 0.339427 25.089490 std 108.278788 115.147216 745.006302 0.092437 1.331103 min 0.034193 0.011535 93218.971693 0.158900 21.933434 25% 105.046718 115.001562 95783.642291 0.266350 23.810732 50% 193.741054 204.499390 96167.556170 0.322751 25.375583 75% 247.078301 263.225130 96572.869616 0.406211 26.174101 max 656.087912 692.232319 97994.424096 0.583680 28.322500 Ev count 732.000000 mean 193.443347 std 111.702355 min 0.022864 25% 110.240736 50% 198.849797 75% 255.228341 max 674.160116 >> Aggregate absolut deviation stats: Ev_left Ev_right P_amb RH_amb T_amb Ev count 732.000000 732.000000 732.000000 732.000000 732.000000 732.000000 mean 81.257046 86.716355 548.861802 0.077279 1.148244 83.985039 std 71.502139 75.689288 503.361706 0.050640 0.671987 73.581792 min 0.120473 0.008023 1.424261 0.000027 0.003348 0.056125 25% 29.963755 30.933747 157.898648 0.035814 0.619595 30.150215 50% 67.815441 73.053771 392.819373 0.071488 1.140245 70.372760 75% 116.557913 125.559855 799.900195 0.112836 1.600374 120.532842 max 468.552941 492.880597 2925.392590 0.244253 3.233010 480.716769 *** Power Measurement Statistics for `pos18`: >> Aggregate daily: E_bat E_in I_bat I_in P_bat \ count 736.000000 736.000000 7.300000e+02 7.300000e+02 7.300000e+02 mean -0.071494 0.190637 -1.999565e-07 -3.129015e-06 -8.392473e-07 std 0.070205 0.103654 1.934449e-07 1.356422e-06 8.119202e-07 min -0.200434 0.000000 -5.527090e-07 -8.029074e-06 -2.319834e-06 25% -0.098648 0.160653 -2.776081e-07 -4.256191e-06 -1.165183e-06 50% -0.070493 0.190575 -1.951107e-07 -3.246802e-06 -8.188995e-07 75% -0.015199 0.267967 -7.357589e-08 -2.706308e-06 -3.088011e-07 max 0.650490 1.001774 1.793838e-06 -1.542990e-09 7.528896e-06 P_in V_bat V_in count 7.300000e+02 730.000000 730.000000 mean 2.239056e-06 4.197107 0.530332 std 1.183438e-06 0.000118 0.245273 min 3.819567e-11 4.196836 0.022699 25% 1.904129e-06 4.197018 0.460511 50% 2.215745e-06 4.197100 0.539188 75% 3.149904e-06 4.197214 0.735353 max 1.159474e-05 4.197400 0.838615 >> Aggregate absolut deviation stats: E_bat E_in I_bat I_in P_bat \ count 736.000000 736.000000 7.300000e+02 7.300000e+02 7.300000e+02 mean 0.050929 0.076525 1.399711e-07 1.033048e-06 5.874847e-07 std 0.048285 0.069857 1.334247e-07 8.781972e-07 5.600023e-07 min 0.000002 0.000043 7.511254e-11 1.437955e-10 2.800661e-10 25% 0.012787 0.012824 3.487774e-08 3.436244e-07 1.463853e-07 50% 0.037382 0.066546 1.021164e-07 8.771507e-07 4.286090e-07 75% 0.080884 0.119872 2.260719e-07 1.376659e-06 9.488553e-07 max 0.721983 0.811137 1.993795e-06 4.900059e-06 8.368143e-06 P_in V_bat V_in count 7.300000e+02 7.300000e+02 730.000000 mean 8.703616e-07 9.676083e-05 0.194887 std 8.012223e-07 6.809654e-05 0.148747 min 6.202647e-10 1.675618e-08 0.000203 25% 1.626028e-07 3.182544e-05 0.062534 50% 7.354306e-07 9.567623e-05 0.170271 75% 1.338114e-06 1.486899e-04 0.264050 max 9.355682e-06 2.926396e-04 0.507633 *** Sensor Measurement Statistics for `pos18`: >> Aggregate daily: Ev_left Ev_right P_amb RH_amb T_amb \ count 730.000000 730.000000 730.000000 730.000000 730.000000 mean 20.707549 20.319931 96031.624590 0.338396 24.852169 std 9.403978 9.368834 744.098229 0.100460 0.630170 min 0.000012 0.000000 93122.522355 0.125753 22.945598 25% 17.629221 17.353349 95676.481506 0.255916 24.330189 50% 21.848526 21.064573 96057.287686 0.322168 25.023772 75% 28.553698 28.326649 96457.218543 0.419248 25.326318 max 45.059619 46.178464 97899.126560 0.600718 26.152201 Ev count 730.000000 mean 20.513740 std 9.381824 min 0.000006 25% 17.495025 50% 21.470127 75% 28.437325 max 45.619042 >> Aggregate absolut deviation stats: Ev_left Ev_right P_amb RH_amb T_amb Ev count 730.000000 730.000000 730.000000 730.000000 730.000000 730.000000 mean 7.242740 7.192307 548.652273 0.085755 0.533609 7.206910 std 5.992125 5.997901 502.244897 0.052232 0.334643 6.000652 min 0.010327 0.010822 0.717342 0.000619 0.003624 0.003664 25% 2.580161 2.486702 156.400139 0.045316 0.267438 2.414002 50% 5.986102 6.325535 390.452723 0.082313 0.498683 6.020811 75% 9.454843 9.557643 798.057955 0.120713 0.745559 9.519470 max 24.352070 25.858533 2909.102235 0.262323 1.906571 25.105301