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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