count_plants_with_generation = 0
#for plant_id,plant in powerwatch_database.iteritems():
# if plant.generation != pw.NO_DATA_OTHER:
# count_plants_with_generation += 1
#print('Of {0} total plants, {1} have reported generation data.'.format(len(powerwatch_database),count_plants_with_generation))
print('Estimating generation...')
estimated_plants = pw.estimate_generation(powerwatch_database)
print('...estimated for {0} plants.'.format(estimated_plants))
# STEP 6: Write PowerWatch
for dbname, data in database_additions.iteritems():
print("Added {0} plants ({1} MW) from {2}.".format(data['count'], data['capacity'], dbname))
f_log.close()
print("Loaded {0} plants to PowerWatch.".format(len(powerwatch_database)))
pw.write_csv_file(powerwatch_database,POWERWATCH_CSV_SAVEFILE)
print("PowerWatch built.")
# STEP 7: Dump Data
if DATA_DUMP:
print("Dumping all the data...")
# STEP 7.1: Label plants in datadump
pw_idnrs = powerwatch_database.keys()
for plant_id,plant in powerwatch_datadump.iteritems():
if plant_id in pw_idnrs:
plant.idnr = plant_id + ",Yes"
else:
plant.idnr = plant_id + ",No"
# STEP 7.2: Add unused CARMA plants
for plant_id,plant in carma_database.iteritems():
longitude = float(row[longitude_col])
except:
coord_skip_count += 1
continue
country = row[
country_col] # note: this is the ISO3 code so no need to convert
# assign ID number
idnr = pw.make_id(SAVE_CODE, idval)
new_location = pw.LocationObject(pw.NO_DATA_UNICODE, latitude,
longitude)
new_plant = pw.PowerPlant(plant_idnr=idnr,
plant_name=name,
plant_country=country,
plant_location=new_location,
plant_source=SOURCE_NAME,
plant_source_url=SOURCE_URL)
plants_dictionary[idnr] = new_plant
# report on plants read from file
print(u"...read {0} plants.".format(len(plants_dictionary)))
print("Skipped {0} plants because of missing lat/long coordinates.".format(
coord_skip_count))
# write database to csv format
pw.write_csv_file(plants_dictionary, CSV_FILE_NAME)
#
# save database
pw.save_database(plants_dictionary, SAVE_CODE, SAVE_DIRECTORY)
print(u"Pickled database to {0}".format(SAVE_DIRECTORY))
country = plant_info[0]
fuel_index = plant_info[1]
fuel_type = fuel_type_list[fuel_index]
capacity = plant_info[2]
year = plant_info[3]
fuel_av_cf = average_capacity_factors[country][fuel_type]
plants_for_generation_estimation[plant_id].append(fuel_av_cf)
cap_sh_country = capacity / float(capacity_total_by_country[country])
plants_for_generation_estimation[plant_id].append(cap_sh_country)
cap_sh_country_fuel = capacity / float(
capacity_by_country_by_fuel[country][fuel_type])
plants_for_generation_estimation[plant_id].append(cap_sh_country_fuel)
# now make prediction; translate from cf to generation (GWh)
X_data = np.array(plants_for_generation_estimation[plant_id][1:]).reshape(
1, -1)
est_cf = est.predict(X_data)[0]
if est_cf < 0 or est_cf > 1:
print(u'ERROR: Estimated capacity factor outside of [0,1]')
est_gen_gwh = est_cf * capacity / CF_CONVERSION_FACTOR
plants[plant_id].estimated_generation_gwh = est_gen_gwh
# now write the result
pw.write_csv_file(plants, CSV_SAVEFILE)
print(u"Wrote data file with {0} total plants; {1} with estimated generation.".
format(len(plants), len(plants_for_generation_estimation)))