def run_schedule_by_schedule_comparison(time_range, predicted_pv_data_path, real_pv_data_path):
control_sim_name = f"baseline_pred"
control_oemof_file_name = 'baseline_pred.oemof'
run_simulation(time_range, predicted_pv_data_path, 'hal_plugin/data/sh_intensity=15_area=46.5.csv',
control_sim_name, control_oemof_file_name)
hal_schedule_file_name = 'hal_offline'
oemoef_schedule_file_name = 'oemof_offline'
run_both_with_existing_schedule(control_oemof_file_name, control_sim_name, time_range, real_pv_data_path,
hal_schedule_file_name,
oemoef_schedule_file_name)
# plot control
plot_results(control_sim_name, oemof_file=control_oemof_file_name, show=False)
# plot schedule sim
oemof_dir = Path.cwd() / 'oemof_runs' / 'results'
hal_scheduled_results = get_oemof_results(oemof_dir, hal_schedule_file_name, exclude_storage=True)
oemof_scheduled_results = get_oemof_results(oemof_dir, oemoef_schedule_file_name, exclude_storage=True)
oemof_schedule_costs = load_oemof_costs(oemof_scheduled_results)
hal_schedule_costs = load_oemof_costs(hal_scheduled_results)
fig, axes = plt.subplots(nrows=3)
mixed_costs = pd.DataFrame({'hal_KWh': hal_schedule_costs['wh'] / 1000, 'oemof_KWh': oemof_schedule_costs['wh'] / 1000,
'KWh_diff': oemof_schedule_costs['wh'] / 1000 - hal_schedule_costs['wh'] / 1000},
index=oemof_schedule_costs.index)
mixed_costs.plot(title=f"Imported KWh", ax=axes[0])
mixed_costs.cumsum().plot(title='Cummulated Imported KWh', ax=axes[1])
mixed_costs.sum().plot.bar(title='Total KWh Comparison', ax=axes[2], grid=True)
plt.show()
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 get_baseline(dec_setup_path):
oemof_baseline = load_oemof_costs(
get_oemof_results(dec_setup_path, 'baseline_pred.oemof', True))
hal_baseline = hal_load_import_kwh(dec_setup_path / 'baseline_pred',
'baseline_pred')
oemof_dec = load_oemof_costs(
get_oemof_results(dec_setup_path, 'real_data_offline.oemof', True))
hal_dec = hal_load_import_kwh(dec_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_dec = hal_dec['wh_total'] / 1000
total_oemof_dec = oemof_dec['wh_total'] / 1000
return total_hal_baseline, total_oemof_baseline, total_hal_dec, total_oemof_dec
def get_imports(oemof_dir, oemof_file, hal_dir, hal_sim_name, split_max=None):
if split_max is not None:
oemof_results = get_oemof_split_results(oemof_dir, split_max)
else:
oemof_results = get_oemof_results(oemof_dir,
oemof_file,
exclude_storage=True)
hal_import = hal_load_import_kwh(hal_dir, hal_sim_name)
oemof_import = load_oemof_costs(oemof_results)
return oemof_import, hal_import
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)
from plotting.oemof_data_processing import load_oemof_costs
from plotting.plot_run import get_oemof_results, to_percentiles, params_from_hal_path, params_from_oemof_path
base_path = Path.cwd() / 'result_data' / 'noise setup - sept 1w'
hal_dir = base_path / 'hal variance'
oemof_dir = base_path / 'oemof variance'
hal_dirs = [dir for dir in os.listdir(hal_dir) if re.search('_([0-9\.]+)_([0-9\.]+)_([0-9])+$', dir) is not None]
oemof_file_names = [file_name for file_name in os.listdir(oemof_dir) if re.search('_([0-9\.]+)_([0-9\.]+)_([0-9])+.oemof', file_name) is not None]
oemof_control_results = get_oemof_results(base_path, 'ensemble_control.oemof', exclude_storage=True)
hal_costs = [(dir, hal_load_import_kwh(hal_dir / dir, dir)['wh'].sum() / 1000) for dir in hal_dirs]
hal_costs_by_param, hal_mean, hal_percentiles = to_percentiles(hal_costs, params_from_hal_path)
oemof_costs = [(file_name, load_oemof_costs(get_oemof_results(oemof_dir, file_name, exclude_storage=True))['wh'].sum() / 1000)
for file_name in oemof_file_names]
oemof_costs_by_param, oemof_mean, oemof_percentiles = to_percentiles(oemof_costs, params_from_oemof_path)
stds = [t[1] for t in hal_costs_by_param.keys()]
fig, axes = plt.subplots(1, 1)
axes.fill_between(stds, hal_percentiles[0], hal_percentiles[1], alpha=0.4)
axes.plot(stds, hal_mean, 'x-')
axes.fill_between(stds, oemof_percentiles[0], oemof_percentiles[1], alpha=0.4)
axes.plot(stds, oemof_mean, 'x-')
axes.set_xlabel('$\sigma$')
axes.set_ylabel('KWh')
axes.legend(['HAL controlled', 'schedule controlled'])
fig.subplots_adjust(left=0.12, right=0.97, top=0.92, bottom=0.11, hspace=0.38)
