MCA Bilanz
Bibliotheken importieren
## Bibliotheken importieren
# Plot im Notebook darstellen
%matplotlib inline
# libs zur Dateibearbeitung
import os
import sys
from pathlib import Path
# Name des aktuellen Notebooks für die exportierten Datein
file_name = "MCA_Speicherverluste"
# Unterordner für die exportierten Dateien
output_folder = 'output_files'
# Ordner auf dem Server (nach files/)
ftp_folder = 'Berechnungen/Auswertungen'
# Unterordner erstellen, wenn dieser noch nicht besteht
Path(output_folder).mkdir(parents=True, exist_ok=True)
# Dokumentenpfad
doc_path = os.getcwd()
import gc
import psutil
process = psutil.Process(os.getpid())
from tqdm import tqdm, tqdm_notebook
import pandas as pd
pd.options.display.max_rows = None
pd.options.display.max_columns = None
pd.options.display.html.table_schema = True
import matplotlib.patches as mpatches
import matplotlib.dates as mdates
from matplotlib.sankey import Sankey
#from matplotlib import rcParams
from calendar import monthrange
import matplotlib.font_manager
from IPython.display import display, Markdown
import IPython
from IPython.display import display
# setzt den defaultwert für Arraya auf float32 anstatt float64
import numpy as np
_oldarray = np.array
def array32(*args, **kwargs):
if 'dtype' not in kwargs:
kwargs.update(dtype=float32)
_oldarray(*args, **kwargs)
np.array = _oldarray
import datetime as dt
from datetime import datetime
# Skriptstartzeitpunkt
startTime = datetime.now()
import locale
#locale.setlocale(locale.LC_TIME, "de_DE")
#locale.setlocale(locale.LC_ALL, 'de_DE.utf8')
from tabulate import tabulate
import matplotlib as mpl
mpl.rcParams['font.family'] = 'Linux Libertine'
import matplotlib.pyplot as plt
## Plotten
mpl.rcParams['text.usetex'] = True
mpl.rcParams['text.latex.preamble']=[r"\usepackage{underscore}"]
#mpl.rcParams['text.latex.preamble']=[r"\usepackage{amsmath}"]
mpl.rcParams['axes.unicode_minus']=False
mpl.rcParams.update({'font.size': 8})
mpl.rcParams['axes.linewidth'] = 0.5
mpl.rcParams['legend.fancybox'] = False
mpl.rcParams['axes.linewidth'] = 0.5
mpl.rcParams['savefig.dpi'] = 330
mpl.rcParams['figure.dpi'] = 330
mpl.rcParams['figure.autolayout'] = False
mpl.rcParams['figure.facecolor'] = 'w'
## Print as Markdown
def print_md(string):
display(Markdown(string))
## Das Jahr unter die Monate schrieben, funktioniert aber mit der aktuellen Formatierung nicht mehr
def year_under_month(label):
month = label.month_name(locale ='German')[:3]
if (month == 'Jan') |( month == 'Mai'):
month += f'\n{label.year}'
return monthDaten importieren
## Daten importieren
## Durchlauf mit 15 Sekunden Auflösung dauert 5 Minuten
## Auflösung der Messdaten in Sekunden / Stunde in Sekunden
res = 3600/3600
daten = pd.read_csv('../1_Daten/Arbeitsdaten/Daten_1H.csv')
daten.set_index(['Zeit'], inplace=True)
daten.index = pd.to_datetime(daten.index)
#daten.isna().sum()
#display(daten.columns)
## dfs erstellen um sie füllen zu können
Speicher_Mittel = pd.DataFrame().astype(np.float32)
Energie_Tag = pd.DataFrame().astype(np.float32)
Energie_Monat = pd.DataFrame().astype(np.float32)
Energie_Jahr = pd.DataFrame(index=['1']).astype(np.float32)Funktionen
# Gauß`sche Addition
####################
def unc_2sign(u1=1.,u2=1.):
uges = np.sqrt((u1*u1) + (u2*u2))
return uges
# Berechnung der stochastischen und systematischen Messfehler
#############################################################
def unc_Q_combined(name, Th,uth,usysh, Tc,utc,usysc, md,u_md,usysmd, cp, dt, dist,nr,f_sigma,plot):
## Notwendig, um den moving_average aus der Funktion zu bekommen
movemean = pd.DataFrame().astype(np.float32)
DF_MEAN = pd.DataFrame().astype(np.float32)
# Berechnung der stochastischen Unsicherheiten:
Qt,u_Qt, Qday,u_Qday, Qmonth,u_Qmonth, Qyear,u_Qyear = unc_Q_stochastisch(T1=Th,ut1=uth,T2=Tc,ut2=utc,md=md,u_md=u_md,cp=cp,dt=dt, dist=dist)
# Berechnung der systematischen Unsicherheiten
u_Qday_sys,u_Qmonth_sys,u_Qyear_sys,qsys_mean,movemean = unc_Q_systematisch (T1=Th,usys1=usysh, T2=Tc,usys2=usysc, md=md,usysmd=usysmd, cp=cp, dt=dt, dist=dist,nr=nr,f_sigma=f_sigma,plot=plot)
# Kombinieren der stochastischen und systematischen Messfehler
u_Qday_comb = unc_2sign(u_Qday, u_Qday_sys)
u_Qmonth_comb = unc_2sign(u_Qmonth, u_Qmonth_sys)
u_Qyear_comb = unc_2sign(u_Qyear, u_Qyear_sys)
Energie_Tag [name + '_Wert'] = Qday
Energie_Tag [name + '_Fehler'] = u_Qday_comb
Energie_Monat[name+'_Wert'] = Qmonth
Energie_Monat[name+'_Fehler'] = u_Qmonth_comb
Energie_Jahr[name+'_Wert'] = Qyear
Energie_Jahr[name+'_Fehler'] = u_Qyear_comb
return np.round(Qyear,3), np.round(u_Qyear_comb,3),np.round(u_Qyear,3), np.round(u_Qyear_sys,3), np.round(qsys_mean,5), movemean
# Berechnung der stochastischen Messfehler
##########################################
def unc_Q_stochastisch(T1,ut1, T2,ut2, md,u_md, cp, dt, dist):
## maximaler stochsastischer Fehler
if dist == 'max_error':
dT = np.maximum(T1-T2,0)
#dT = T1-T2
Qt = md*cp*dT*dt
dT_max = dT + ut1 + ut2
md_max = md + u_md
Qt_max = md_max*cp*dT_max*dt
u_Qt = Qt_max - Qt
Qday = Qt.resample('D').sum()
u_Qday = u_Qt.resample('D').sum()
Qmonth = Qt.resample('M').sum()
u_Qmonth = u_Qt.resample('M').sum()
Qyear = Qt.resample('Y').sum().sum() # angepasst um jahresübergreifen zu summieren
u_Qyear = u_Qt.resample('Y').sum().sum() # angepasst um jahresübergreifen zu summieren
else: ## Normal oder Gleichverteilung
## Berechnung nur mit positiven Temperaturdifferenzen, damit der Vergleich stimmt.
dT = np.maximum(T1-T2,0)
#dT = T1-T2
u_dT = unc_2sign(u1=ut1,u2=ut2)
Qt = md*cp*dT*dt
u_Qt = np.sqrt((((md>0)*cp*dt*dT*u_md)**2+(cp*dt*md*u_dT)**2))
u_Qt2 = u_Qt*u_Qt
Qday = Qt.resample('D').sum()
u_Qday = np.sqrt(u_Qt2.resample('D').sum())
Qmonth = Qt.resample('M').sum()
u_Qmonth = np.sqrt(u_Qt2.resample('M').sum())
Qyear = Qt.resample('Y').sum().sum() # angepasst um jahresübergreifen zu summieren
u_Qyear = np.sqrt(u_Qt2.resample('Y').sum().sum()) # angepasst um jahresübergreifen zu summieren
return (np.round(Qt,decimals=3), np.round(u_Qt,decimals=3),
np.round(Qday,decimals=3), np.round(u_Qday,decimals=3),
np.round(Qmonth,decimals=3), np.round(u_Qmonth,decimals=3),
np.round(Qyear,decimals=3), np.round(u_Qyear,decimals=3))
# Berechnung der systematischen Messfehler
##########################################
def unc_Q_systematisch(T1,usys1,T2,usys2,md,usysmd,cp,dt,dist,nr,f_sigma,plot):
## Vergleichsberechnung
#Q_single = (md*cp*np.maximum(T1-T2,0)*dt).resample('Y').sum().sum()
## maximaler systematischer Fehler
if dist == 'max_error':
dT = T1 - T2
Qt = md*cp*dT*dt
dT_max = dT + usys1 + usys2
md_max = md + usysmd
Qt_max = md_max*cp*dT_max*dt
uQsys_max = Qt_max - Qt
Qday = Qt.resample('D').sum()
uQday_sys = uQsys_max.resample('D').sum()
Qmonth = Qt.resample('M').sum()
uQmonth_sys = uQsys_max.resample('M').sum()
Qyear = Qt.resample('Y').sum().sum() # angepasst um jahresübergreifend zu summieren
uQyear_sys = uQsys_max.resample('Y').sum().sum() # angepasst um jahresübergreifend zu summieren
# werden bei MAX nicht berechnet, müssen aber zurückgegeben werden
qsys_mean = uQyear_sys
movmean = 0
sigma_mov = 0
movemean = movmean
else:
## Zufallszahlen generieren
rand1 = np.random.RandomState(seed = 456)
rand2 = np.random.RandomState(seed = 8822)
rand3 = np.random.RandomState(seed = 42)
rand4 = np.random.RandomState(seed = 4432)
if dist == 'uniform': ## gleichverteilt
uTh = rand1.uniform(-usys1, usys1, nr)
uTc = rand2.uniform(-usys2, usys2 , nr)
umd = rand3.uniform(-usysmd, usysmd, nr)
else: ## normalverteilt
udT = rand4.normal(0, (usys1 / f_sigma), nr)
uTh = rand1.normal(0, (usys1 / f_sigma), nr)
uTc = rand2.normal(0, (usys2 / f_sigma), nr)
umd = rand3.normal(0, (usysmd / f_sigma), nr)
# Liste_Mean_Runde = []
# zähler = 0
Th = 0
Tc = 0
dT0 = 0
for i in range(0,nr,1):
if dist == 'getrennt':
# beide Temperaturen bekommen eine systematiche Unsicherheit
Th = T1 + uTh[i]
Tc = T2 + uTc[i]
dT = np.maximum(Th-Tc,0)
else:
## nur die Temperaturdifferenz bekommt eine systematische Unsicherheit
dT0 = np.maximum(T1-T2,0)
dT = dT0 + udT[i]
dT = np.maximum(dT,0)
md_manip = (md * (1+(umd[i]/100.)))## wenn dT + udT negaviv wird, wieder auf 0 setzen
## Berechne Q
Q_tmp = md_manip*cp*dT*dt
i_neg = np.where(Q_tmp<0)[0]
if len(i_neg)>1:
print('Negative Energien vorhanden! '+str(i))
Q_tmp[i_neg]=0
tmp_str = "Q_{0}".format(i)
if i == 0:
Q = pd.DataFrame(Q_tmp, index=dT.index).astype(np.float32)#MemoryError:
Q.columns = [tmp_str]
else:
Q[tmp_str] = Q_tmp.values.astype(np.float32) #MemoryError: Unable to allocate array with shape (892, 2088412) and data type float64
# # Überprüfung der bislang erreichten Genauigkeit und Abbruch der Runden
# if i > 100 and zähler > 0 or i%50 == 0:
# Liste_Summe_Runde = []
# for element in Q.columns:
# Liste_Summe_Runde.append(np.sum(Q[element]))
# Mean_Runde = np.mean(Liste_Summe_Runde)
# Liste_Mean_Runde.append(Mean_Runde)
# Sigm_Runde = np.std(Liste_Mean_Runde[-int(i/3):])
# # Abbruchbedingung für Rundendurchlauf
# # Q 1/100 von Q_single
# # sigma 1/100 von Q_single
# if Q_single*0.99 <= Mean_Runde <= Q_single*1.01 and Sigm_Runde <= 0.01* Q_single:
# zähler = zähler + 1
# else:
# zähler = 0
# print('{0} Q/Q:{1} %_Sigma:{2} Zähler:{3}'.format(i, round(Q_single/Mean_Runde*100,2), round(Sigm_Runde/Mean_Runde*100,2) ,zähler))
# if zähler >= 100:
# nr = i
# break
# print(Liste_Summe_Runde)
del [[Q_tmp, Th, Tc, md_manip, dT, dT0, tmp_str ]]; gc.collect()
Qday = Q.resample('D').sum().astype(np.float32)
uQday_sys = Qday.std(axis=1)
Qmonth = Q.resample('M').sum().astype(np.float32)
uQmonth_sys = Qmonth.std(axis=1)
Qyear = Q.resample('Y').sum().sum().astype(np.float32) # angepasst um jahresübergreifend zu summieren
uQyear_sys = Qyear.std(axis=0) # angepasst um jahresübergreifend zu summieren
list_qsys = []
for element in Q.columns:
list_qsys.append(np.sum(Q[element]))
#display(list_qsys)
# display(Q.info())
del Q; gc.collect()
#display('Q.: '+str(round(process.memory_info().rss/1024/1024/1024,4))+' GB')
qsys_mean = np.mean(list_qsys)
movmean = []
sigma_mov = []
for i in range(0,nr,1):
movmean.append(np.mean(list_qsys[0:(i+1)]))
sigma_mov.append(np.std(list_qsys[0:(i+1)]))
movemean = movmean # for return
del list_qsys; gc.collect()
if plot==1:
# plot
fig, ax = plt.subplots(figsize=(6, 3), dpi=150)
# moving lines
plt.plot(np.arange(0,nr,1),movmean, color='xkcd:darkish blue', linestyle='--', linewidth=0.8, label = r'gleitender Durchschnitt: $Q$')
plt.plot(np.arange(0,nr,1),qsys_mean+sigma_mov, color='xkcd:darkish red', linestyle='--', linewidth=0.8, label = r'gleitender Durchschnitt: $u_{sys}$')
plt.plot(np.arange(0,nr,1),qsys_mean-sigma_mov, color='xkcd:darkish red', linestyle='--', linewidth=0.8, label = '')
# result of moving lines
plt.axhline(y=int(round(qsys_mean,1)), color='xkcd:darkish blue', linestyle='-', linewidth=0.8, label = r'Durchschnitt: $Q$')
plt.axhline(y=int(round(qsys_mean,1) + round(uQyear_sys,1)), color='xkcd:darkish red', linestyle='-', linewidth=0.8, label = r'Durchschnitt: $u_{sys}$')
plt.axhline(y=int(round(qsys_mean,1) - round(uQyear_sys,1)), color='xkcd:darkish red', linestyle='-', linewidth=0.8, label = '')
# Text for result of moving lines right side
# ax.text(1.02, qsys_mean, str(int(qsys_mean)) +' kWh', va='center', ha="left", transform=ax.get_yaxis_transform())
# ax.text(1.02, qsys_mean+uQyear_sys, str(int(qsys_mean+uQyear_sys))+' kWh', va='center', ha="left", transform=ax.get_yaxis_transform())
# ax.text(1.02, qsys_mean-uQyear_sys, str(int(qsys_mean-uQyear_sys))+' kWh', va='center', ha="left", transform=ax.get_yaxis_transform())
ax.text(1.02, qsys_mean+uQyear_sys/2, '+'+str(int(round(uQyear_sys,1)))+' kWh \n+'+str(round(uQyear_sys/qsys_mean*100,1))+'\%' , va='center', ha="left", fontsize=6, color='black', transform=ax.get_yaxis_transform())
ax.text(1.02, qsys_mean-uQyear_sys/2, '-'+str(int(round(uQyear_sys,1)))+' kWh \n-'+str(round(uQyear_sys/qsys_mean*100,1))+'\%' , va='center', ha="left", fontsize=6, color='black', transform=ax.get_yaxis_transform())
ax.set_title(name)
ax.set_ylabel(r'\textbf{Wärmemenge} (kWh)')
ax.set_xlabel(r'\textbf{Wiederholungen} (n)')
ax.xaxis.set_tick_params(width=0.5)
ax.yaxis.set_tick_params(width=0.5)
## Beschriftung der Y Achse nur mit den Ergebniswerten
ax.get_yaxis().set_ticks([int(qsys_mean),int(qsys_mean+uQyear_sys),int(qsys_mean-uQyear_sys)])
ax.set_xlim(0, nr)
h0, l0 = ax.get_legend_handles_labels() #, bbox_to_anchor=(1, .85)
legend = plt.legend(h0, l0, borderpad=0.15,labelspacing=0.1, frameon=True, edgecolor="black", ncol=2, loc='upper right',framealpha=1, facecolor='white')
legend.get_frame().set_linewidth(0.3)
for line in legend.get_lines():
line.set_linewidth(1.5)
plt.tight_layout()
plt.savefig(output_folder + '/' + file_name + '_MCA.pdf', bbox_inches='tight')
plt.savefig(output_folder + '/' + file_name + '_MCA.png', bbox_inches='tight')
plt.show()
#del [[movmean, sigma_mov ]]; gc.collect()
return uQday_sys,uQmonth_sys,uQyear_sys,qsys_mean, movemean# Qyear = 500
# Energie_Jahr = pd.DataFrame(index=['1']).astype(np.float32)
# Energie_Jahr[name+'_Wert'] = Qyear
# Energie_Jahr[name+'_Wedrt'] = Qyear
# Energie_Jahr
Energieberechnung
Speichermitteltemperatur stochastisch
## Ungenauigkeiten Speichermitteltemperatur
## Höhenanteile
H1 = 0.2415
H2 = 0.2317
H3 = 0.1935
H4 = 0.1725
H5 = 0.1606
## Ungenauigkeiten Höhenanteile
U_H = 0.1
U_H1 = H1 * U_H
U_H2 = H2 * U_H
U_H3 = H3 * U_H
U_H4 = H4 * U_H
U_H5 = H5 * U_H
## Temperaturen
T1 = daten["S_Speicher_1"]
T2 = daten["S_Speicher_2"]
T3 = daten["S_Speicher_3"]
T4 = daten["S_Speicher_4"]
T5 = daten["S_Speicher_5"]
## Ungenauigkeiten Temperatur
U_T = 0.8
U_T1 = T1 * U_T
U_T2 = T2 * U_T
U_T3 = T3 * U_T
U_T4 = T4 * U_T
U_T5 = T5 * U_T
## Mitteltemperatur
T_M_1 = H1 * T1
T_M_2 = H2 * T2
T_M_3 = H3 * T3
T_M_4 = H4 * T4
T_M_5 = H5 * T5
T_M = T_M_1 + T_M_2 + T_M_3 + T_M_4 + T_M_5
## Ungenauigkeit Mitteltemperatur
U_T_M_1 = np.sqrt(pow(H1 * U_T1 ,2) + pow(U_H1 * T1 ,2))
U_T_M_2 = np.sqrt(pow(H2 * U_T2 ,2) + pow(U_H2 * T2 ,2))
U_T_M_3 = np.sqrt(pow(H3 * U_T3 ,2) + pow(U_H3 * T3 ,2))
U_T_M_4 = np.sqrt(pow(H4 * U_T4 ,2) + pow(U_H4 * T4 ,2))
U_T_M_5 = np.sqrt(pow(H5 * U_T5 ,2) + pow(U_H5 * T5 ,2))
U_T_M = np.sqrt(pow(U_T_M_1,2) + pow(U_T_M_2,2) + pow(U_T_M_3,2) + pow(U_T_M_4,2) + pow(U_T_M_5,2))
daten['S_Speicher_Mitteltemperatur'] = T_M
daten['S_Speicher_Mitteltemperatur_vorher'] = daten['S_Speicher_Mitteltemperatur'].shift()
daten["u_Speicher_Mitteltemperatur"] = U_T_M
daten['u_Speicher_Mitteltemperatur_vorher'] = U_T_M.shift()
## in den DataFrame
Speicher_Mittel['Temp1'] = T_M
Speicher_Mittel['sto'] = U_T_M
# für die nächste Zelle
u_Speicher_Mitteltemperatur = daten['u_Speicher_Mitteltemperatur'].resample("Y").mean().mean()
print("alles Jahresdurchschnitt")
print("Speicher Mitteltemperatur: "+str(round(daten['S_Speicher_Mitteltemperatur'].resample("Y").mean().mean(),2)))
print("stochastische Unsicherheit: "+str(round(daten['u_Speicher_Mitteltemperatur'].resample("Y").mean().mean(),2)))
print("in Prozent: "+str(round((daten['u_Speicher_Mitteltemperatur'] / daten['S_Speicher_Mitteltemperatur'] * 100).resample("Y").mean().mean(),2)))
alles Jahresdurchschnitt
Speicher Mitteltemperatur: 52.17
stochastische Unsicherheit: 19.86
in Prozent: 38.39Speichermitteltemperatur systematisch
# Berechnung der systematischen Messfehler
##########################################
def unc_TM_systematisch(H1,H2,H3,H4,H5,T1,T2,T3,T4,T5,usys_H,usys_T, nr):
rand0 = np.random.RandomState(seed = 8822)
rand1 = np.random.RandomState(seed = 5429)
rand2 = np.random.RandomState(seed = 7845)
rand3 = np.random.RandomState(seed = 6257)
rand4 = np.random.RandomState(seed = 4432)
rand5 = np.random.RandomState(seed = 4689)
rand6 = np.random.RandomState(seed = 2357)
rand7 = np.random.RandomState(seed = 1254)
rand8 = np.random.RandomState(seed = 6587)
rand9 = np.random.RandomState(seed = 2145)
u_H1 = rand0.normal(0, (usys_H / 2), nr)
u_H2 = rand1.normal(0, (usys_H / 2), nr)
u_H3 = rand2.normal(0, (usys_H / 2), nr)
u_H4 = rand3.normal(0, (usys_H / 2), nr)
u_H5 = rand4.normal(0, (usys_H / 2), nr)
u_T1 = rand5.normal(0, (usys_T / 2), nr)
u_T2 = rand6.normal(0, (usys_T / 2), nr)
u_T3 = rand7.normal(0, (usys_T / 2), nr)
u_T4 = rand8.normal(0, (usys_T / 2), nr)
u_T5 = rand9.normal(0, (usys_T / 2), nr)
for i in range(0,nr,1):
T1_sys = T1 + u_T1[i]
T2_sys = T2 + u_T2[i]
T3_sys = T3 + u_T3[i]
T4_sys = T4 + u_T4[i]
T5_sys = T5 + u_T5[i]
H1_sys = H1 + u_H1[i]
H2_sys = H2 + u_H2[i]
H3_sys = H3 + u_H3[i]
H4_sys = H4 + u_H4[i]
H5_sys = H5 + u_H5[i]
TM_tmp = T1_sys*H1_sys+T2_sys*H2_sys+T3_sys*H3_sys+T4_sys*H4_sys+T5_sys*H5_sys
tmp_str = "Q_{0}".format(i)
if i == 0:
Q = pd.DataFrame(TM_tmp, index=T1_sys.index).astype(np.float32)#MemoryError:
Q.columns = [tmp_str]
else:
Q[tmp_str] = TM_tmp.values.astype(np.float32) #MemoryError: Unable to allocate array with shape (892, 2088412) and data type float6
Qday = Q.mean(axis=1)
uQday_sys = Q.std(axis=1)
del [[TM_tmp, tmp_str ]]; gc.collect()
return Qday,uQday_sys
## Höhenanteile
H1 = 0.2415
H2 = 0.2317
H3 = 0.1935
H4 = 0.1725
H5 = 0.1606
## Ungenauigkeiten Höhenanteile
usys_H = 0.1
## Temperaturen
T1 = daten["S_Speicher_1"]
T2 = daten["S_Speicher_2"]
T3 = daten["S_Speicher_3"]
T4 = daten["S_Speicher_4"]
T5 = daten["S_Speicher_5"]
## Ungenauigkeiten Temperatur
usys_T = 0.8
nr = 1000
TM,uTM_sys = unc_TM_systematisch(H1,H2,H3,H4,H5,T1,T2,T3,T4,T5,usys_H,usys_T,nr)
Speicher_Mittel['Temp'] = TM
Speicher_Mittel['sys'] = uTM_sys
TM.head() Zeit
2017-05-01 00:00:00 58.213749
2017-05-01 01:00:00 57.419991
2017-05-01 02:00:00 57.832981
2017-05-01 03:00:00 58.612064
2017-05-01 04:00:00 57.865639
dtype: float32# Gauß`sche Addition
####################
def unc_2sign(u1=1.,u2=1.):
uges = np.sqrt((u1*u1) + (u2*u2))
return uges
Speicher_Mittel['com'] = unc_2sign(Speicher_Mittel['sto'] ,Speicher_Mittel['sys'])
Speicher_Mittel['diff'] = Speicher_Mittel['Temp'] - Speicher_Mittel['Temp1']
Speicher_Mittel.head()| Temp1 | sto | Temp | sys | com | diff | |
|---|---|---|---|---|---|---|
| Zeit | ||||||
| 2017-05-01 00:00:00 | 57.894031 | 21.640103 | 58.213749 | 6.438913 | 22.577725 | 0.319718 |
| 2017-05-01 01:00:00 | 57.107195 | 21.074505 | 57.419991 | 6.363337 | 22.014242 | 0.312796 |
| 2017-05-01 02:00:00 | 57.516419 | 21.390094 | 57.832981 | 6.402353 | 22.327702 | 0.316562 |
| 2017-05-01 03:00:00 | 58.291762 | 21.770275 | 58.612064 | 6.495847 | 22.718735 | 0.320302 |
| 2017-05-01 04:00:00 | 57.550510 | 21.331340 | 57.865639 | 6.411593 | 22.274080 | 0.315129 |
Plot Speichermitteltemperatur inkl sto & sys
## Plotten
plt.rc('text', usetex=True)
plt.rc('text.latex', preamble=r'\usepackage{underscore}')
plt.rc('font', family='serif')
plt.rcParams.update({'font.size': 8})
plt.rcParams['font.serif'] = "Linux Libertine"
plt.rcParams['axes.linewidth'] = 0.5
plt.rcParams['legend.fancybox'] = False
plt.rcParams['axes.linewidth'] = 0.5
fig, ax = plt.subplots(figsize=(6, 3.5), dpi=330)
Speicher_Mittel['Temp'].plot(ax=ax,color='xkcd:flat blue', linewidth=0.5, alpha = 1, label = 'Speicher Mitteltemperatur')
Speicher_Mittel['sys'].plot(ax=ax,color='xkcd:darkgreen',linewidth=0.5,alpha = 1,label = 'sys Unsicherheit')
Speicher_Mittel['sto'].plot(ax=ax,color='xkcd:darkish red',linewidth=0.5,alpha = 1,label = 'sto Unsicherheit')
Speicher_Mittel['com'].plot(ax=ax,color='xkcd:butterscotch',linewidth=0.5,alpha = 1,label = 'comb. Unsicherheit')
ax.set_ylabel(r'\textbf{Temperatur} ($^\circ$C)')
ax2 = ax.twinx()
# Speicher_Mittel['diff'].plot(ax=ax2,color='xkcd:darkgreen',linewidth=0.8,alpha = 0.8,label = 'diff')
# ax2.set_ylabel(r'(\%)')
ax.set(xlim=[pd.to_datetime("05.01.17 00:00:00"),pd.to_datetime("30.04.18 23:00:00")])
ax.set(ylim=0)
# ax2.set(ylim=0)
ax.set_xlabel('')
ax.xaxis.set_tick_params(width=0.5)
ax.yaxis.set_tick_params(width=0.5)
ax2.yaxis.set_tick_params(width=0.5)
h0, l0 = ax.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
legend = plt.legend(h0+h2, l0+l2, borderpad=0.15,labelspacing=0.1, frameon=True, edgecolor="black", ncol=1, loc=1,framealpha=1, facecolor='white')
legend.get_frame().set_linewidth(0.3)
plt.tight_layout()
plt.savefig(output_folder + '/' + file_name + '.pdf', bbox_inches='tight')
plt.savefig(output_folder + '/' + file_name + '.png', bbox_inches='tight')
plt.show()
Speicherverluste MCA
name = 'Speicherverluste_MCA'
Th = daten.S_Speicher_Mitteltemperatur
uth = u_Speicher_Mitteltemperatur
usysh = uth / 2
Tc = daten.S_Dachbodentemperatur
utc = 3.5
usysc = 1.7
md = daten.S_Speicher_UA
u_md = 0.0
usysmd = 1.1
cp = (4.2/3.6) / 1000.0
dt = res
dist = "normal"
f_sigma = 2.0
nr = 2000
plot = 1
# Funktionsaufruf dist=getrennt für unabhängige Unsicherheiten der Temperaturen
Qyear,u_Qyear_comb,u_Qyear,u_Qyear_sys, qsys_mean, movemean = unc_Q_combined(name,Th,uth,usysh,Tc,utc,usysc,md,u_md,usysmd,1,res,'getrennt',2000,2.0,1)
# Vergleichsrechnung
Q_single = (md*1*np.maximum(Th-Tc,0)*dt).resample('Y').sum().sum()
Markdown('**'+name+'** <br />'
'Verteilung: **'+dist+'**, \u03C3: **'+str(f_sigma)+'**, Runden: **'+str(nr)+'**, Datensätze: **'+str(Th.count())+'**, <span class="burk">Zeitschritt</span>: **'+str(int(dt*3600))+'s** <br />'
'Wärmemenge Gesamtunsicherheit: **'+str(int(Qyear.sum()))+' kWh \u00B1 '+str(int(u_Qyear_comb))+'** kWh <br />'
'deterministische Unsicherheit: \u00B1 '+str(round(u_Qyear_sys,3))+' kWh <br />'
'stochastische Unsicherheit: \u00B1 '+str(round(u_Qyear,3))+' kWh <br />'
'Verhältnis von Referenz zu Q: **'+str(round(Q_single/qsys_mean,5))+'** <br />'
'Variationskoeffizient letztes 1/3:**'+str(round( (np.std(movemean[-int(nr/3):]) / np.mean(movemean[-int(nr/3):]) ), 6))+'**<br />' )
Speicherverluste_MCA
Verteilung: normal, σ: 2.0, Runden: 2000, Datensätze: 8705, Zeitschritt: 3600s
Wärmemenge Gesamtunsicherheit: 1916 kWh ± 317 kWh
deterministische Unsicherheit: ± 317.625 kWh
stochastische Unsicherheit: ± 13.991 kWh
Verhältnis von Referenz zu Q: 0.99821
Variationskoeffizient letztes 1/3:0.00062
Ende der Berechnungen
daten.S_Speicher_UAZeit
2017-05-01 00:00:00 0.00930
2017-05-01 01:00:00 0.00930
2017-05-01 02:00:00 0.00930
2017-05-01 03:00:00 0.00930
2017-05-01 04:00:00 0.00930
2017-05-01 05:00:00 0.00930
2017-05-01 06:00:00 0.00930
2017-05-01 07:00:00 0.00930
2017-05-01 08:00:00 0.00930
2017-05-01 09:00:00 0.00930
2017-05-01 10:00:00 0.00930
2017-05-01 11:00:00 0.00930
2017-05-01 12:00:00 0.00930
2017-05-01 13:00:00 0.00930
2017-05-01 14:00:00 0.00930
2017-05-01 15:00:00 0.00930
2017-05-01 16:00:00 0.00930
2017-05-01 17:00:00 0.00930
2017-05-01 18:00:00 0.00930
2017-05-01 19:00:00 0.00930
2017-05-01 20:00:00 0.00930
2017-05-01 21:00:00 0.00930
2017-05-01 22:00:00 0.00930
2017-05-01 23:00:00 0.00930
2017-05-02 00:00:00 0.00930
2017-05-02 01:00:00 0.00930
2017-05-02 02:00:00 0.00930
2017-05-02 03:00:00 0.00930
2017-05-02 04:00:00 0.00930
2017-05-02 05:00:00 0.00930
2017-05-02 06:00:00 0.00930
2017-05-02 07:00:00 0.00930
2017-05-02 08:00:00 0.00930
2017-05-02 09:00:00 0.00930
2017-05-02 10:00:00 0.00930
2017-05-02 11:00:00 0.00930
2017-05-02 12:00:00 0.00930
2017-05-02 13:00:00 0.00930
2017-05-02 14:00:00 0.00930
2017-05-02 15:00:00 0.00930
2017-05-02 16:00:00 0.00930
2017-05-02 17:00:00 0.00930
2017-05-02 18:00:00 0.00930
2017-05-02 19:00:00 0.00930
2017-05-02 20:00:00 0.00930
2017-05-02 21:00:00 0.00930
2017-05-02 22:00:00 0.00930
2017-05-02 23:00:00 0.00930
2017-05-03 00:00:00 0.00930
2017-05-03 01:00:00 0.00930
2017-05-03 02:00:00 0.00930
2017-05-03 03:00:00 0.00930
2017-05-03 04:00:00 0.00930
2017-05-03 05:00:00 0.00930
2017-05-03 06:00:00 0.00930
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2017-05-03 08:00:00 0.00930
2017-05-03 09:00:00 0.00930
2017-05-03 10:00:00 0.00930
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2017-05-03 13:00:00 0.00930
2017-05-03 14:00:00 0.00930
2017-05-03 15:00:00 0.00930
2017-05-03 16:00:00 0.00930
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2017-05-03 19:00:00 0.00930
2017-05-03 20:00:00 0.00930
2017-05-03 21:00:00 0.00930
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2017-05-03 23:00:00 0.00930
2017-05-04 00:00:00 0.00930
2017-05-04 01:00:00 0.00930
2017-05-04 02:00:00 0.00930
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2017-05-12 00:00:00 0.00930
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Name: S_Speicher_UA, dtype: float64Meta
## Skriptlaufzeit
now = datetime.now()
duration = now - startTime
hours = duration.seconds // 3600
minutes = (duration.seconds % 3600) // 60
seconds = (duration.seconds % 60)## NACH BEENDIGUNG DER BERECHNUNG MUSS DAS NOTEBOOK GESPEICHERT
## UND ANSCHLIEßEND DIESE ZELLE AUSGEFÜHRT WERDEN !
## workaround nach Beendigung der Berechnung, um diese Datei zu publizieren.
## speichern als Markdown, Bilder einbetten,
## auf den Server laden, Transfer kontrollieren,
## Link erzeugen, Skriptlaufzeit ausgeben
## als Markdown Datei speichern
## mit nbconvert das aktuelle Notebook als Markdown Datei im Ordner Markdown_Files speichern
## alle Abbildungen liegen im Unterordner Dateiname_files
os.system('jupyter nbconvert --to markdown ' + file_name + '.ipynb --output-dir ' + output_folder + ' --output ' + file_name + '_exIm.md')
## die in der Markdowndatei verlinkten Abbildungen als "data:image/png;base64" in die Datei einbetten
## https://github.com/freeman-lab/embed-images
## in den output Ordner wechseln
os.chdir(output_folder)
os.system('embed-images ' + file_name + '_exIm.md > ' + file_name + '_emb.md')
os.chdir(doc_path)
## FTP Upload
from ftplib import FTP
## config file with server, user & password
sys.path.append(os.path.abspath('../template'))
import FTP_config
## Server Login and change folder
ftp = FTP(FTP_config.login['server'])
ftp.login(FTP_config.login['user'], FTP_config.login['password'])
ftp.cwd(ftp_folder)
## upload the _emb.md file as .md file
with open(output_folder + '/'+file_name + '_emb.md', 'rb') as f:
ftp.storbinary('STOR %s' % file_name + '.md', f)
## testing if the new file is on the server
from dateutil import parser
timestamp = ftp.voidcmd('MDTM '+ file_name + '.md')[4:].strip()
file_time = parser.parse(timestamp)
now = datetime.now()
how_old = now - file_time
## 2h (7200s) timedifference to the server, 10s uncertaintie
if how_old.seconds < 7210:
transfer = True
#ftp.retrlines('LIST')
ftp.quit()
## making link, if file is transfered
if transfer:
from IPython.core.display import display, HTML
display(HTML('<a target="_blank" style="text-decoration:none; font-size:20px; padding-left: 10px;" href="https://bootshaus.rasal.de/'+ftp_folder+'/'+file_name+'"> ' + file_name + '</a>'))
print ('Datum & Uhrzeit: '+now.strftime("%d.%m.%Y %H:%M:%S"))
print ('Skriptlaufzeit: '+str(minutes)+' Minuten '+str(seconds)+' Sekunden')---------------------------------------------------------------------------
error_perm Traceback (most recent call last)
<ipython-input-12-5b36d13fb2f7> in <module>
29 ftp = FTP(FTP_config.login['server'])
30 ftp.login(FTP_config.login['user'], FTP_config.login['password'])
---> 31 ftp.cwd(ftp_folder)
32
33 ## upload the _emb.md file as .md file
c:\users\kolja\appdata\local\programs\python\python37\lib\ftplib.py in cwd(self, dirname)
629 dirname = '.' # does nothing, but could return error
630 cmd = 'CWD ' + dirname
--> 631 return self.voidcmd(cmd)
632
633 def size(self, filename):
c:\users\kolja\appdata\local\programs\python\python37\lib\ftplib.py in voidcmd(self, cmd)
276 """Send a command and expect a response beginning with '2'."""
277 self.putcmd(cmd)
--> 278 return self.voidresp()
279
280 def sendport(self, host, port):
c:\users\kolja\appdata\local\programs\python\python37\lib\ftplib.py in voidresp(self)
249 def voidresp(self):
250 """Expect a response beginning with '2'."""
--> 251 resp = self.getresp()
252 if resp[:1] != '2':
253 raise error_reply(resp)
c:\users\kolja\appdata\local\programs\python\python37\lib\ftplib.py in getresp(self)
244 raise error_temp(resp)
245 if c == '5':
--> 246 raise error_perm(resp)
247 raise error_proto(resp)
248
error_perm: 550 Berechnungen/Auswertungen: Datei oder Verzeichnis nicht gefunden
