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How to use load_profile method in hypothesis
Best Python code snippet using hypothesis
Bill_Calc_old.py
Source:Bill_Calc_old.py 
1import numpy as np2import pandas as pd3import time4import sys5def bill_calculator(load_profile, tariff):6 def pre_processing_load(load_profile):7 # placeholder for a quick quality check function for load profile8 # make sure it is kwh9 # make sure it is one year10 # make sure it doesn't have missing value or changing the missing values to zero or to average11 # make sure the name is Load12 # time interval is half hour13 return load_profile14 def fr_calc(load_profile, tariff):15 f_load_profile = load_profile16 imports = [np.nansum(f_load_profile[col].values[f_load_profile[col].values > 0])17 for col in f_load_profile.columns if col != 'READING_DATETIME']18 Results = pd.DataFrame(index=[col for col in f_load_profile.columns if col != 'READING_DATETIME'],19 data=imports, columns=['Annual_kWh'])20 Results['Annual_kWh_exp'] = [-1 * np.nansum(f_load_profile[col].values[f_load_profile[col].values < 0])21 for col in f_load_profile.columns if col != 'READING_DATETIME']22 if tariff['ProviderType'] == 'Retailer':23 Results['DailyCharge'] = len(load_profile.index.normalize().unique()) * tariff['Parameters']['Daily']['Value']24 Results['EnergyCharge'] = Results['Annual_kWh'] * tariff['Parameters']['Energy']['Value']25 Results['EnergyCharge_Discounted'] = Results['EnergyCharge'] * (1 - tariff['Discount (%)'] / 100)26 Results['Fit_Rebate'] = Results['Annual_kWh_exp'] * tariff['Parameters']['FiT']['Value']27 Results['Bill'] = Results['DailyCharge'] + Results['EnergyCharge'] - Results['Fit_Rebate']28 else:29 for TarComp, TarCompVal in tariff['Parameters'].items():30 Results[TarComp, 'DailyCharge'] = len(load_profile.index.normalize().unique()) * TarCompVal['Daily']['Value']31 Results[TarComp, 'EnergyCharge'] = Results['Annual_kWh'] * TarCompVal['Energy']['Value']32 Results['Bill'] = Results['NUOS','DailyCharge'] + Results['NUOS','EnergyCharge']33 return Results34 def block_annual(load_profile, tariff):35 f_load_profile = load_profile36 imports = [np.nansum(f_load_profile[col].values[f_load_profile[col].values > 0])37 for col in f_load_profile.columns if col != 'READING_DATETIME']38 Results = pd.DataFrame(index=[col for col in f_load_profile.columns if col != 'READING_DATETIME'],39 data=imports, columns=['Annual_kWh'])40 Results['Annual_kWh_exp'] = [-1 * np.nansum(f_load_profile[col].values[f_load_profile[col].values < 0])41 for col in f_load_profile.columns if col != 'READING_DATETIME']42 if tariff['ProviderType'] == 'Retailer':43 tariff_temp = tariff.copy()44 del tariff_temp['Parameters']45 tariff_temp['Parameters'] = {'Retailer': tariff['Parameters']}46 tariff = tariff_temp.copy()47 for TarComp, TarCompVal in tariff['Parameters'].items():48 Results[TarComp, 'DailyCharge'] = len(load_profile.index.normalize().unique()) * TarCompVal['Daily'][49 'Value']50 BlockUse = Results[['Annual_kWh']].copy()51 BlockUseCharge = Results[['Annual_kWh']].copy()52 lim = 053 for k, v in TarCompVal['Energy'].items():54 BlockUse[k] = BlockUse['Annual_kWh']55 BlockUse[k][BlockUse[k] > v['HighBound']]= v['HighBound']56 BlockUse[k] = BlockUse[k]-lim57 BlockUse[k][BlockUse[k] < 0] = 058 lim = v['HighBound']59 BlockUseCharge[k] = BlockUse[k] * v['Value']60 del BlockUse['Annual_kWh']61 del BlockUseCharge['Annual_kWh']62 Results[TarComp, 'EnergyCharge'] = BlockUseCharge.sum(axis=1)63 if 'Discount (%)' in tariff:64 Results[TarComp, 'EnergyCharge_Discounted'] = Results[TarComp, 'EnergyCharge'] * (65 1 - tariff['Discount (%)'] / 100)66 else:67 Results[TarComp, 'EnergyCharge_Discounted'] = Results[TarComp, 'EnergyCharge']68 if 'FiT' in TarCompVal:69 Results[TarComp, 'Fit_Rebate'] = Results['Annual_kWh_exp'] * TarCompVal['FiT']['Value']70 else:71 Results[TarComp, 'Fit_Rebate'] = 072 if tariff['ProviderType'] == 'Retailer':73 Results['Bill'] = Results['Retailer', 'DailyCharge'] + Results['Retailer', 'EnergyCharge_Discounted'] - \74 Results['Retailer', 'Fit_Rebate']75 else:76 Results['Bill'] = Results['NUOS', 'DailyCharge'] + Results['NUOS', 'EnergyCharge_Discounted'] - Results[77 'NUOS', 'Fit_Rebate']78 return Results79 def block_quarterly(load_profile, tariff):80 load_profile_imp=load_profile.clip_lower(0)81 load_profile_Q1 = load_profile_imp.loc[load_profile_imp.index.month.isin([1, 2, 3]), :]82 load_profile_Q2 = load_profile_imp.loc[load_profile_imp.index.month.isin([4, 5, 6]), :]83 load_profile_Q3 = load_profile_imp.loc[load_profile_imp.index.month.isin([7, 8, 9]), :]84 load_profile_Q4 = load_profile_imp.loc[load_profile_imp.index.month.isin([10, 11, 12]), :]85 f_load_profile = load_profile86 imports = [np.nansum(f_load_profile[col].values[f_load_profile[col].values > 0])87 for col in f_load_profile.columns if col != 'READING_DATETIME']88 Results = pd.DataFrame(index=[col for col in f_load_profile.columns if col != 'READING_DATETIME'],89 data=imports, columns=['Annual_kWh'])90 Results['Q1_kWh'] = load_profile_Q1.sum()91 Results['Q2_kWh'] = load_profile_Q2.sum()92 Results['Q3_kWh'] = load_profile_Q3.sum()93 Results['Q4_kWh'] = load_profile_Q4.sum()94 Results['Annual_kWh_exp'] = [-1 * np.nansum(f_load_profile[col].values[f_load_profile[col].values < 0])95 for col in f_load_profile.columns if col != 'READING_DATETIME']96 if tariff['ProviderType'] == 'Retailer':97 tariff_temp = tariff.copy()98 del tariff_temp['Parameters']99 tariff_temp['Parameters'] = {'Retailer': tariff['Parameters']}100 tariff = tariff_temp.copy()101 for TarComp, TarCompVal in tariff['Parameters'].items():102 Results[TarComp, 'DailyCharge'] = len(load_profile.index.normalize().unique()) * TarCompVal['Daily'][103 'Value']104 for i in range(1,5):105 BlockUse = Results[['Q{}_kWh'.format(i)]].copy()106 BlockUseCharge = BlockUse.copy()107 lim = 0108 for k, v in TarCompVal['Energy'].items():109 BlockUse[k] = BlockUse['Q{}_kWh'.format(i)]110 BlockUse[k][BlockUse[k] > v['HighBound']] = v['HighBound']111 BlockUse[k] = BlockUse[k] - lim112 BlockUse[k][BlockUse[k] < 0] = 0113 lim = v['HighBound']114 BlockUseCharge[k] = BlockUse[k] * v['Value']115 del BlockUse['Q{}_kWh'.format(i)]116 del BlockUseCharge['Q{}_kWh'.format(i)]117 Results[TarComp, 'EnergyCharge_Q{}'.format(i)] = BlockUseCharge.sum(axis=1)118 Results[TarComp, 'EnergyCharge'] = Results[TarComp, 'EnergyCharge_Q1'] +Results[TarComp, 'EnergyCharge_Q2']\119 +Results[TarComp, 'EnergyCharge_Q3']+Results[TarComp, 'EnergyCharge_Q4']120 if 'Discount (%)' in tariff:121 Results[TarComp, 'EnergyCharge_Discounted'] = Results[TarComp, 'EnergyCharge'] * (122 1 - tariff['Discount (%)'] / 100)123 else:124 Results[TarComp, 'EnergyCharge_Discounted'] = Results[TarComp, 'EnergyCharge']125 if 'FiT' in TarCompVal:126 Results[TarComp, 'Fit_Rebate'] = Results['Annual_kWh_exp'] * TarCompVal['FiT']['Value']127 else:128 Results[TarComp, 'Fit_Rebate'] = 0129 if tariff['ProviderType'] == 'Retailer':130 Results['Bill'] = Results['Retailer', 'DailyCharge'] + Results['Retailer', 'EnergyCharge_Discounted'] - \131 Results['Retailer', 'Fit_Rebate']132 else:133 Results['Bill'] = Results['NUOS', 'DailyCharge'] + Results['NUOS', 'EnergyCharge_Discounted'] - Results[134 'NUOS', 'Fit_Rebate']135 return Results136 def tou_calc(load_profile, tariff):137 t0 = time.time()138 f_load_profile = load_profile139 imports = [np.nansum(f_load_profile[col].values[f_load_profile[col].values > 0])140 for col in f_load_profile.columns if col != 'READING_DATETIME']141 Results = pd.DataFrame(index=[col for col in f_load_profile.columns if col != 'READING_DATETIME'],142 data=imports, columns=['Annual_kWh'])143 Results['Annual_kWh_exp'] = [-1 * np.nansum(f_load_profile[col].values[f_load_profile[col].values < 0])144 for col in f_load_profile.columns if col != 'READING_DATETIME']145 if tariff['ProviderType'] == 'Retailer':146 tariff_temp = tariff.copy()147 del tariff_temp['Parameters']148 tariff_temp['Parameters'] = {'Retailer': tariff['Parameters']}149 tariff = tariff_temp.copy()150 for TarComp, TarCompVal in tariff['Parameters'].items():151 Results[TarComp,'DailyCharge'] = len(load_profile.index.normalize().unique()) * TarCompVal['Daily']['Value']152 time_ind = np.zeros(load_profile.shape[0])153 load_profile_TI = pd.DataFrame()154 load_profile_TI_Charge = pd.DataFrame()155 ti = 0156 for k, v in TarCompVal['Energy'].items():157 this_part = v.copy()158 ti += 1159 for k2, v2, in this_part['TimeIntervals'].items():160 start_hour = int(v2[0][0:2])161 if start_hour == 24:162 start_hour = 0163 start_min = int(v2[0][3:5])164 end_hour = int(v2[1][0:2])165 if end_hour == 0:166 end_hour = 24167 end_min = int(v2[1][3:5])168 if this_part['Weekday']:169 if start_hour <= end_hour:170 time_ind = np.where((load_profile.index.weekday < 5) &171 (load_profile.index.month.isin(this_part['Month'])) &172 (((60 * load_profile.index.hour + load_profile.index.minute)173 >= (60 * start_hour + start_min)) &174 ((60 * load_profile.index.hour + load_profile.index.minute)175 < (60 * end_hour + end_min))), ti,176 time_ind)177 else:178 time_ind = np.where((load_profile.index.weekday < 5) &179 (load_profile.index.month.isin(this_part['Month'])) &180 (((60 * load_profile.index.hour + load_profile.index.minute)181 >= (60 * start_hour + start_min)) |182 ((60 * load_profile.index.hour + load_profile.index.minute)183 < (60 * end_hour + end_min))), ti,184 time_ind)185 if this_part['Weekend']:186 if start_hour <= end_hour:187 time_ind = np.where((load_profile.index.weekday >= 5) &188 (load_profile.index.month.isin(this_part['Month'])) &189 (((60 * load_profile.index.hour + load_profile.index.minute)190 >= (60 * start_hour + start_min)) &191 ((60 * load_profile.index.hour + load_profile.index.minute)192 < (60 * end_hour + end_min))), ti,193 time_ind)194 else:195 time_ind = np.where((load_profile.index.weekday >= 5) &196 (load_profile.index.month.isin(this_part['Month'])) &197 (((60 * load_profile.index.hour + load_profile.index.minute)198 >= (60 * start_hour + start_min)) |199 ((60 * load_profile.index.hour + load_profile.index.minute)200 < (60 * end_hour + end_min))), ti,201 time_ind)202 load_profile_TI[k] = load_profile.loc[time_ind == ti, :].sum()203 load_profile_TI_Charge[k] = this_part['Value'] * load_profile_TI[k]204 Results[TarComp,'EnergyCharge'] = load_profile_TI_Charge.sum(axis=1)205 if 'Discount (%)' in tariff:206 Results[TarComp,'EnergyCharge_Discounted'] = Results[TarComp,'EnergyCharge'] * (1 - tariff['Discount (%)'] / 100)207 else:208 Results[TarComp,'EnergyCharge_Discounted'] = Results[TarComp,'EnergyCharge']209 if 'FiT' in TarCompVal:210 Results[TarComp, 'Fit_Rebate'] = Results['Annual_kWh_exp'] * TarCompVal['FiT']['Value']211 else:212 Results[TarComp, 'Fit_Rebate'] = 0213 if tariff['ProviderType'] == 'Retailer':214 Results['Bill'] = Results['Retailer','DailyCharge'] + Results['Retailer','EnergyCharge_Discounted'] - Results['Retailer','Fit_Rebate']215 else:216 Results['Bill'] = Results['NUOS','DailyCharge'] + Results['NUOS','EnergyCharge_Discounted'] - Results['NUOS','Fit_Rebate']217 print(time.time() - t0)218 return Results219 def demand_charge(load_profile, tariff):220 f_load_profile = load_profile221 imports = [np.nansum(f_load_profile[col].values[f_load_profile[col].values > 0])222 for col in f_load_profile.columns if col != 'READING_DATETIME']223 Results = pd.DataFrame(index=[col for col in f_load_profile.columns if col != 'READING_DATETIME'],224 data=imports, columns=['Annual_kWh'])225 Results['Annual_kWh_exp'] = [-1 * np.nansum(f_load_profile[col].values[f_load_profile[col].values < 0])226 for col in f_load_profile.columns if col != 'READING_DATETIME']227 if tariff['ProviderType'] == 'Retailer':228 tariff_temp = tariff.copy()229 del tariff_temp['Parameters']230 tariff_temp['Parameters'] = {'Retailer': tariff['Parameters']}231 tariff = tariff_temp.copy()232 for TarComp, TarCompVal in tariff['Parameters'].items():233 Results[TarComp, 'DailyCharge'] = len(load_profile.index.normalize().unique()) * TarCompVal['Daily'][234 'Value']235 if ('Unit', '$/kWh') in TarCompVal['Energy'].items():236 Results[TarComp, 'EnergyCharge'] = Results['Annual_kWh'] * TarCompVal['Energy']['Value']237 else:238 load_profile_imp = load_profile.clip_lower(0)239 load_profile_Q1 = load_profile_imp.loc[load_profile_imp.index.month.isin([1, 2, 3]), :]240 load_profile_Q2 = load_profile_imp.loc[load_profile_imp.index.month.isin([4, 5, 6]), :]241 load_profile_Q3 = load_profile_imp.loc[load_profile_imp.index.month.isin([7, 8, 9]), :]242 load_profile_Q4 = load_profile_imp.loc[load_profile_imp.index.month.isin([10, 11, 12]), :]243 Results['Q1_kWh'] = load_profile_Q1.sum()244 Results['Q2_kWh'] = load_profile_Q2.sum()245 Results['Q3_kWh'] = load_profile_Q3.sum()246 Results['Q4_kWh'] = load_profile_Q4.sum()247 for i in range(1, 5):248 BlockUse = Results[['Q{}_kWh'.format(i)]].copy()249 BlockUseCharge = BlockUse.copy()250 lim = 0251 for k, v in TarCompVal['Energy'].items():252 BlockUse[k] = BlockUse['Q{}_kWh'.format(i)]253 BlockUse[k][BlockUse[k] > v['HighBound']] = v['HighBound']254 BlockUse[k] = BlockUse[k] - lim255 BlockUse[k][BlockUse[k] < 0] = 0256 lim = v['HighBound']257 BlockUseCharge[k] = BlockUse[k] * v['Value']258 del BlockUse['Q{}_kWh'.format(i)]259 del BlockUseCharge['Q{}_kWh'.format(i)]260 Results[TarComp, 'EnergyCharge_Q{}'.format(i)] = BlockUseCharge.sum(axis=1)261 Results[TarComp, 'EnergyCharge'] = Results[TarComp, 'EnergyCharge_Q1'] + Results[TarComp, 'EnergyCharge_Q2'] \262 + Results[TarComp, 'EnergyCharge_Q3'] + Results[263 TarComp, 'EnergyCharge_Q4']264 if 'Discount (%)' in tariff:265 Results[TarComp, 'EnergyCharge_Discounted'] = Results[TarComp, 'EnergyCharge'] * (266 1 - tariff['Discount (%)'] / 100)267 else:268 Results[TarComp, 'EnergyCharge_Discounted'] = Results[TarComp, 'EnergyCharge']269 if 'FiT' in TarCompVal:270 Results[TarComp, 'Fit_Rebate'] = Results['Annual_kWh_exp'] * TarCompVal['FiT']['Value']271 else:272 Results[TarComp, 'Fit_Rebate'] = 0273 Results[TarComp, 'Demand'] = 0274 Results[TarComp, 'DemandCharge'] = 0275 for DemCharComp, DemCharCompVal in TarCompVal['Demand'].items():276 TSNum = DemCharCompVal['Demand Window Length'] # number of timestamp277 NumofPeaks = DemCharCompVal['Number of Peaks']278 if TSNum > 1:279 load_profile_r = load_profile.rolling(TSNum, min_periods=1).mean()280 else:281 load_profile_r = load_profile282 time_ind = np.zeros(load_profile.shape[0])283 ti = 1284 for k2, v2, in DemCharCompVal['TimeIntervals'].items():285 start_hour = int(v2[0][0:2])286 if start_hour == 24:287 start_hour = 0288 start_min = int(v2[0][3:5])289 end_hour = int(v2[1][0:2])290 if end_hour == 0:291 end_hour = 24292 end_min = int(v2[1][3:5])293 if DemCharCompVal['Weekday']:294 if start_hour <= end_hour:295 time_ind = np.where((load_profile.index.weekday < 5) &296 (load_profile.index.month.isin(DemCharCompVal['Month'])) &297 (((60 * load_profile.index.hour + load_profile.index.minute)298 >= (60 * start_hour + start_min)) &299 ((60 * load_profile.index.hour + load_profile.index.minute)300 < (60 * end_hour + end_min))), ti,301 time_ind)302 else:303 time_ind = np.where((load_profile.index.weekday < 5) &304 (load_profile.index.month.isin(DemCharCompVal['Month'])) &305 (((60 * load_profile.index.hour + load_profile.index.minute)306 >= (60 * start_hour + start_min)) |307 ((60 * load_profile.index.hour + load_profile.index.minute)308 < (60 * end_hour + end_min))), ti,309 time_ind)310 if DemCharCompVal['Weekend']:311 if start_hour <= end_hour:312 time_ind = np.where((load_profile.index.weekday >= 5) &313 (load_profile.index.month.isin(DemCharCompVal['Month'])) &314 (((60 * load_profile.index.hour + load_profile.index.minute)315 >= (60 * start_hour + start_min)) &316 ((60 * load_profile.index.hour + load_profile.index.minute)317 < (60 * end_hour + end_min))), ti,318 time_ind)319 else:320 time_ind = np.where((load_profile.index.weekday >= 5) &321 (load_profile.index.month.isin(DemCharCompVal['Month'])) &322 (((60 * load_profile.index.hour + load_profile.index.minute)323 >= (60 * start_hour + start_min)) |324 ((60 * load_profile.index.hour + load_profile.index.minute)325 < (60 * end_hour + end_min))), ti,326 time_ind)327 load_profile_r = load_profile_r.loc[time_ind == ti, :]328 load_profile_f = load_profile_r.copy()329 load_profile_f = load_profile_f.reset_index()330 load_profile_f = pd.melt(load_profile_f, id_vars=['READING_DATETIME'],331 value_vars=[x for x in load_profile_f.columns if x != 'READING_DATETIME'])332 load_profile_f = load_profile_f.rename(columns={'variable': 'HomeID', 'value': 'kWh'})333 load_profile_f['Month'] = pd.to_datetime(load_profile_f['READING_DATETIME']).dt.month334 AveragePeaks = 2 * load_profile_f[['HomeID', 'kWh', 'Month']].groupby(['HomeID', 'Month']).apply(335 lambda x: x.sort_values('kWh', ascending=False)[:NumofPeaks]).reset_index(drop=True).groupby(336 ['HomeID']).mean()337 Results[TarComp, 'Demand'] = Results[TarComp, 'Demand'] + AveragePeaks['kWh']338 Results[TarComp, 'DemandCharge'] = Results[TarComp, 'DemandCharge'] + AveragePeaks['kWh'] * DemCharCompVal['Value']339 # AllAveragePeaks = AllAveragePeaks.append(AveragePeaks)340 if tariff['ProviderType'] == 'Retailer':341 Results['Bill'] = Results['Retailer', 'DailyCharge'] + Results['Retailer', 'EnergyCharge_Discounted'] + \342 Results['NUOS', 'DemandCharge'] - Results['Retailer', 'Fit_Rebate']343 else:344 Results['Bill'] = Results['NUOS', 'DailyCharge'] + Results['NUOS', 'EnergyCharge_Discounted'] + \345 Results['NUOS', 'DemandCharge'] - Results['NUOS', 'Fit_Rebate']346 return Results347 # Checking the type and run the appropriate function348 load_profile = pre_processing_load(load_profile)349 if tariff['Type'] == 'Flat_rate':350 Results = fr_calc(load_profile, tariff)351 elif tariff['Type'] == 'TOU':352 Results = tou_calc(load_profile, tariff)353 elif tariff['Type'] == 'Block_Annual':354 Results = block_annual(load_profile, tariff)355 elif tariff['Type'] == 'Block_Quarterly':356 Results = block_quarterly(load_profile, tariff)357 elif tariff['Type'] == 'Demand_Charge':358 Results = demand_charge(load_profile, tariff)359 else:360 Results = 'Error'...
models.py
Source:models.py
1from django.core.validators import MaxLengthValidator2from django.contrib.auth.models import AbstractUser3from django.db import models4from django.db.models.signals import post_save5from phonenumber_field.modelfields import PhoneNumberField6class User(AbstractUser):7 is_vendor = models.BooleanField('Vendor', default=False)8 is_engineer = models.BooleanField('Engineer', default=False)9 email = models.EmailField(unique=True)10 USERNAME_FIELD = 'email'11 REQUIRED_FIELDS = ['username']12class Vendor(models.Model):13 name = models.CharField('Vendor Name', max_length=125)14 user = models.OneToOneField('User', on_delete=models.CASCADE)15 address = models.CharField(max_length=1200)16 phone_number = PhoneNumberField(blank=False)17class Engineer(models.Model):18 name = models.CharField('Vendor Name', max_length=125)19 user = models.OneToOneField('User', on_delete=models.CASCADE)20 address = models.CharField(max_length=1200)21 phone_number = PhoneNumberField(blank=False)22 years_of_experience = models.IntegerField()23class Project(models.Model):24 user = models.ForeignKey('User', on_delete=models.CASCADE)25 load_profile = models.OneToOneField(26 'LoadProfile', on_delete=models.SET_NULL, null=True)27TYPE_CHOICE = (28 ('AC', 'AC'),29 ('DC', 'DC')30)31class Load(models.Model):32 load_name = models.CharField(max_length=120)33 load_rating = models.FloatField()34 quantity = models.IntegerField()35 hourly_usage = models.FloatField()36 weekly_usage = models.FloatField()37 profile_type = models.CharField(choices=TYPE_CHOICE, max_length=2)38 inverter_efficiency = models.FloatField(blank=True, null=True, default=0.9)39 total_wattage_hourly = models.FloatField()40 load_profile = models.ForeignKey(41 'LoadProfile', related_name='loads', on_delete=models.CASCADE, null=True)42 def save(self, *args, **kwargs):43 total_usage = (self.load_rating *44 self.hourly_usage * self.weekly_usage * self.quantity) / (7)45 self.total_wattage_hourly = total_usage46 super().save(*args, **kwargs)47 def __str__(self):48 return f'{self.load_name} for {self.load_profile} loadProfile'49class LoadProfile(models.Model):50 total_demand = models.FloatField(default=0)51 total_ac_demand = models.FloatField(default=0)52 total_dc_demand = models.FloatField(default=0)53 user = models.ForeignKey('User', on_delete=models.CASCADE)54 name = models.CharField(max_length=120)55 peak_sun_hours = models.FloatField(default=3.5)56 battery_capacity = models.FloatField(default=0)57 inverter_rating = models.FloatField(default=0)58 inverter_efficiency = models.FloatField(blank=True, null=True)59 array_sizing = models.FloatField(default=0)60 cable_sizing = models.FloatField(default=0)61 no_of_panels = models.FloatField(default=0)62 panel_output = models.FloatField(default=0)63 def save(self, *args, **kwargs):64 self.inverter_rating = (self.total_demand + (self.total_demand * 0.3))65 return super().save(*args, **kwargs)66 def __str__(self):67 return f'{self.name} {self.total_demand}'68class SolarModel(models.Model):69 image = models.ImageField(null=True)70 isc = models.DecimalField("Short circuit current",71 decimal_places=2, max_digits=10)72 pveff = models.DecimalField(73 "PV efficiency", decimal_places=2, max_digits=10)74 power_rating = models.IntegerField()75 plan_file = models.FileField(null=True)76 peak_generation_factor = models.DecimalField(77 decimal_places=2, max_digits=10)78 name = models.CharField(max_length=120)79 def __str__(self):80 return self.name81def calculate_total_load(sender, **kwargs):82 total = 083 ac_total = 084 dc_total = 085 load_profile = kwargs['instance'].load_profile86 loads = load_profile.loads.all()87 IE = load_profile.inverter_efficiency or None88 for load in loads:89 if not IE and load.profile_type == 'AC':90 load_profile.inverter_efficiency = load.inverter_efficiency91 if load.profile_type == 'AC':92 ac_total += load.total_wattage_hourly / load_profile.inverter_efficiency93 if load.profile_type == 'DC':94 dc_total += load.total_wattage_hourly95 load_profile.total_ac_demand = ac_total96 load_profile.total_dc_demand = dc_total97 load_profile.total_demand = dc_total + ac_total98 load_profile.save()...
EnergySystem.py
Source:EnergySystem.py
...28 PVTcapacity = i['size']29 else:30 PVTcapacity = 031 load = Demand(self.simulation_duration)32 load_profile = load.load_profile()33 load_profile_lis.append({'load':[1],'profile':load_profile})34 print('load', sum(load_profile['Energy']))35 # generation profile36 tempdp = load.load_profile()37 net_nondispatchable_load = tempdp['Energy']38 for i, k in enumerate(self.solar):39 generation = GenerationAsset(k['size'],self.simulation_duration,k['type'])40 generation_profile = generation.load_profile()41 generation_profile_lis.append({'type-size':[k['type'],k['size']],'profile':generation_profile})42 net_nondispatchable_load -= generation_profile['Energy']43 print(k['type'], sum(generation.load_profile()['Energy']))44 #dispatchable -- battery45 # positive net_nondispatchable_load means exist load, energy need46 for i in self.dispatchable:47 storage = StorageAsset(net_nondispatchable_load, i[0], i[1],i[2])48 if rhc:49 storage_profile = storage.get_output()50 else:51 storage_profile = storage.get_output()52 net_nondispatchable_load = net_nondispatchable_load - storage_profile53 storage_profile_lis.append({'capacity:power/energy':[i[0],i[1]],'profile':storage_profile,'type':i[2]})54 print('storage',[i[0],i[1]], sum(storage_profile))55 #Market Simulation56 market = Market(net_nondispatchable_load,load_profile_lis,generation_profile_lis,storage_profile_lis,self.solar,self.dispatchable,self.simulation_duration)57 if rhc:...
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- JUnit Tutorial
- TestNG Tutorial
- Webdriver Tutorial
- WebDriverIO Tutorial
- Protractor Tutorial
- Selenium 4 Tutorial
- Jenkins Tutorial
- NUnit Tutorial
- Jest Tutorial
- Playwright Tutorial
- Cypress Tutorial
- PyTest Tutorial
YouTube
You could also refer to video tutorials over LambdaTest YouTube channel to get step by step demonstration from industry experts.
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