def incremental_net_import_analysis():
base_path = Path().cwd() / 'result_data'
incr_path = base_path / 'incremental setup - sept'
oemof_baseline = load_oemof_costs(
get_oemof_results(base_path, 'oemof_baseline_result_pvol.oemof'))
hal_sim_name = 'hal_baseline_result_pvol'
hal_baseline = hal_load_import_kwh(base_path / hal_sim_name, hal_sim_name)
split_24h = load_oemof_costs(
get_oemof_split_results(incr_path / 'sliced_24h', 30))
split_12h = load_oemof_costs(
get_oemof_split_results(incr_path / 'sliced_12h', 60))
split_6h = load_oemof_costs(
get_oemof_split_results(incr_path / 'sliced_6h', 120))
fig, axes = plt.subplots(nrows=2)
total_hal_baseline = hal_baseline['wh_total'] / 1000
total_oemof_baseline = oemof_baseline['wh_total'] / 1000
total_oemof_24h = split_24h['wh_total'] / 1000
total_oemof_12h = split_12h['wh_total'] / 1000
total_oemof_6h = split_6h['wh_total'] / 1000
mixed_costs = pd.DataFrame(
{
'BL HAL': total_hal_baseline,
'BL OEMOF': total_oemof_baseline,
'24h split': total_oemof_24h,
'12h split': total_oemof_12h,
'6h split': total_oemof_6h,
},
index=total_oemof_baseline.index)
mixed_costs.cumsum().plot(ax=axes[0]).set_ylabel('KWh')
total_plot = mixed_costs.sum().plot.bar(ax=axes[1], grid=True)
total_plot.set_ylabel('KWh')
total_plot.set_xlabel('t')
fig.subplots_adjust(left=0.12,
right=0.97,
top=0.92,
bottom=0.19,
hspace=0.53)
plt.savefig("praktikumsbericht/images/splits_total.pdf")
fig, axes = plt.subplots(nrows=1)
mixed_costs.boxplot(grid=True, showfliers=False, ax=axes)
dataframe_to_stat_table(incr_path / 'stats.csv', mixed_costs)
fig.subplots_adjust(left=0.12,
right=0.96,
top=0.93,
bottom=0.07,
hspace=0.20)
# plt.show()
plt.savefig("praktikumsbericht/images/splits_peaks.pdf")
def real_data_net_import_analysis():
base_path = Path().cwd() / 'result_data'
sept_setup_path = base_path / 'schedule vs real - sept'
oemof_baseline = load_oemof_costs(get_oemof_results(sept_setup_path, 'baseline_pred.oemof', True))
hal_baseline = hal_load_import_kwh(sept_setup_path / 'baseline_pred', 'baseline_pred')
oemof_sept = load_oemof_costs(get_oemof_results(sept_setup_path, 'real_data_offline.oemof', True))
hal_sept = hal_load_import_kwh(sept_setup_path / 'real_data_online', 'real_data_online')
total_hal_baseline = hal_baseline['wh_total'] / 1000
total_oemof_baseline = oemof_baseline['wh_total'] / 1000
total_hal_sept = hal_sept['wh_total'] / 1000
total_oemof_sept = oemof_sept['wh_total'] / 1000
mixed_costs = pd.DataFrame({
'Pred HAL': total_hal_baseline,
'Pred OEMOF': total_oemof_baseline,
'Real HAL': total_hal_sept,
'Real OEMOF': total_oemof_sept,
}, index=total_oemof_baseline.index)
fig, axes = plt.subplots(nrows=1)
mixed_costs.sum().plot.bar(ax=axes, grid=True).set_ylabel('KWh')
fig.subplots_adjust(left=0.12, right=0.97, top=0.92, bottom=0.19, hspace=0.53)
plt.savefig("praktikumsbericht/images/sept_real_total.pdf")
dataframe_to_stat_table(sept_setup_path / 'stats.csv', mixed_costs)
hal_stor_load = load_hal_storage_df(sept_setup_path / 'real_data_online', 'real_data_online')[0]['power[W]']
pred_oemof_results = get_oemof_results(sept_setup_path, 'baseline_pred.oemof', exclude_storage=True)
real_oemof_results = get_oemof_results(sept_setup_path, 'real_data_offline.oemof', exclude_storage=True)
oemof_stor_load = real_oemof_results['b1_data'][(('b1', 'sink_storage'), 'flow')] - real_oemof_results['b1_data'][(('source_storage', 'b1'), 'flow')]
pv_pred = pred_oemof_results['b1_data'][(('source_pv', 'b1'), 'flow')]
pv_real = real_oemof_results['b1_data'][(('source_pv', 'b1'), 'flow')]
pl = DataFrame({
'HAL controlled Storage Balance': hal_stor_load,
'Schedule controlled Storage Balance': oemof_stor_load,
'Pred PV Output': pv_pred,
'Actual PV Output': pv_real,
}, index=real_oemof_results['b1_data'].index)
corr = pl.corr()
# print(corr.to_latex())
print(corr.to_latex(open("praktikumsbericht/images/sept_real_correlation.tex", 'w'),
label='t/res/real',
header=['HAL', 'Schedule', 'Pred PV', 'Actual PV'],
caption='Correlation between PV input and storage consumption',
float_format="%.2f"))
fig, axes = plt.subplots(nrows=1)
pl["2016-09-01"].plot(ax=axes)
axes.set_ylabel('W')
plt.savefig("praktikumsbericht/images/sept_real_example.pdf")
# Percentiles
fig, axes = plt.subplots(nrows=1)
boxplot = mixed_costs.boxplot(grid=True, showfliers=False, ax=axes)
boxplot.set_ylabel('KWh')
fig.subplots_adjust(left=0.12, right=0.96, top=0.93, bottom=0.07, hspace=0.20)
plt.savefig("praktikumsbericht/images/sept_real_peaks.pdf")
dataframe_to_stat_table(sept_setup_path / 'stats.csv', mixed_costs)
def real_data_net_import_analysis():
base_path = Path().cwd() / 'result_data'
sept_setup_path = base_path / 'schedule vs real - sept'
dec_setup_path = base_path / 'schedule vs real - dec'
sept_total_hal_baseline, sept_total_oemof_baseline, sept_total_hal, sept_total_oemof = get_baseline(
sept_setup_path)
dec_total_hal_baseline, dec_total_oemof_baseline, dec_total_hal, dec_total_oemof = get_baseline(
dec_setup_path)
mixed_costs = pd.DataFrame(
{
'Sept Pred HAL': sept_total_hal_baseline,
'Sept Pred OEMOF': sept_total_oemof_baseline,
'Sept Real HAL': sept_total_hal,
'Sept Real OEMOF': sept_total_oemof,
'Dec Pred HAL': dec_total_hal_baseline,
'Dec Pred OEMOF': dec_total_oemof_baseline,
'Dec Real HAL': dec_total_hal,
'Dec Real OEMOF': dec_total_oemof,
},
index=pd.date_range('2016-09-01', '2017-01-01', freq='min'))
fig, axes = plt.subplots(nrows=1)
summed = mixed_costs.sum()
summed.plot.bar(ax=axes, grid=True).set_ylabel('KWh')
fig.subplots_adjust(left=0.12,
right=0.97,
top=0.92,
bottom=0.19,
hspace=0.53)
plt.savefig("praktikumsbericht/images/dec_real_total.pdf")
dataframe_to_stat_table(dec_setup_path / 'stats.csv', mixed_costs)
ratios = DataFrame(
{
'HAL Pred/Real': [
summed['Sept Pred HAL'] / summed['Sept Real HAL'],
summed['Dec Pred HAL'] / summed['Dec Real HAL']
],
'OEMOF Pred/Real': [
summed['Sept Pred OEMOF'] / summed['Sept Real OEMOF'],
summed['Dec Pred OEMOF'] / summed['Dec Real OEMOF']
],
'Pred OEMOF/HAL': [
summed['Sept Pred OEMOF'] / summed['Sept Pred HAL'],
summed['Dec Pred OEMOF'] / summed['Dec Pred HAL']
],
'Real OEMOF/HAL': [
summed['Sept Real OEMOF'] / summed['Sept Real HAL'],
summed['Dec Real OEMOF'] / summed['Dec Real HAL']
],
},
index=['September', 'December'])
dataframe_to_stat_table(dec_setup_path / 'ratios.csv', ratios)
diff = ratios[['Pred OEMOF/HAL', 'Real OEMOF/HAL']].diff(axis='columns')[[
'Real OEMOF/HAL'
]].rename({'Real OEMOF/HAL': 'Real OEMOF/HAL - PRED OEMOF/HAL'},
axis='columns')
dataframe_to_stat_table(dec_setup_path / 'diff.csv', diff)
diff.to_latex(
open("praktikumsbericht/images/real_data_diff.tex", 'w'),
label='t/res/diff',
caption='Difference between the ratio of HALs and OEMOFs net import ..',
float_format="%.2f")
# Percentiles
fig, axes = plt.subplots(nrows=1)
boxplot = mixed_costs.boxplot(grid=True, showfliers=False, ax=axes)
boxplot.set_ylabel('kWh')
fig.subplots_adjust(left=0.12,
right=0.96,
top=0.93,
bottom=0.07,
hspace=0.20)
plt.savefig("praktikumsbericht/images/dec_real_peaks.pdf")