<jupyter_start><jupyter_text>Try to compare the PV Watts prediction with exactly biszmuth and tilt<jupyter_code>import numpy as pd
import pandas as pd
import requests 
import matplotlib.pyplot as plt
Breeze = pd.read_csv('../Cleaned_data/new one/309Breeze.csv')
Huffman = pd.read_csv('../Cleaned_data/new one/12800Huffman.csv')
Wildwood = pd.read_csv('../Cleaned_data/new one/1957Wildwood.csv')
Tahneeta = pd.read_csv('../Cleaned_data/new one/10611Tahneeta.csv')
Huffman
list_parameters = {"formt": 'JSON', "api_key": "<KEY>", "system_capacity": 2.9, "module_type": 0, "losses": 14.08,
              "array_type": 0, "tilt": 28, "azimuth": 180, "lat": 61.109, "lon": -149.87, "dataset": 'tmy2'}
json_response = requests.get("https://developer.nrel.gov/api/pvwatts/v6", params = list_parameters).json()
tmy2 = pd.DataFrame(data = json_response['outputs'])
list_parameters = {"formt": 'JSON', "api_key": "<KEY>", "system_capacity": 2.9, "module_type": 0, "losses": 14.08,
              "array_type": 0, "tilt": 28, "azimuth": 180, "lat": 61.109, "lon": -149.87, "dataset": 'tmy3'}
json_response = requests.get("https://developer.nrel.gov/api/pvwatts/v6", params = list_parameters).json()
tmy3 = pd.DataFrame(data = json_response['outputs'])
a = Breeze['Energy Produced (Wh)'][0:364].sum()/1000
b = Breeze['Energy Produced (Wh)'][265:720].sum()/1000
print(a)
print(b)
print(tmy2['ac_annual'][0])
print(tmy3['ac_annual'][0])
list_parameters = {"formt": 'JSON', "api_key": "<KEY>", "system_capacity": 3.6, "module_type": 0, "losses": 14.08,
              "array_type": 0, "tilt": 25, "azimuth": 180, "lat": 61.109, "lon": -149.87, "dataset": 'tmy2'}
json_response = requests.get("https://developer.nrel.gov/api/pvwatts/v6", params = list_parameters).json()
tmy2 = pd.DataFrame(data = json_response['outputs'])

list_parameters = {"formt": 'JSON', "api_key": "<KEY>", "system_capacity": 3.6, "module_type": 0, "losses": 14.08,
              "array_type": 0, "tilt": 25, "azimuth": 180, "lat": 61.109, "lon": -149.87, "dataset": 'tmy2'}
json_response = requests.get("https://developer.nrel.gov/api/pvwatts/v6", params = list_parameters).json()
tmy2 = pd.DataFrame(data = json_response['outputs'])
print(tmy2['ac_annual'][0])
print(tmy3['ac_annual'][0])
a = Huffman['Energy Produced (Wh)'][0:364].sum()/1000
b = Huffman['Energy Produced (Wh)'][365:694].sum()/1000
print(a)
print(b)
plt.plot(Huffman['Energy Produced (Wh)'])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.20940695296523518, 'fraction_numerical': 0.06748466257668712, 'mean_word_length': 5.6287262872628725, 'pattern_counts': {'":': 44, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 4, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '536388', 'license': 'MIT', 'n_tokens_mistral': 1058, 'n_tokens_neox': 938, 'n_words': 185, 'path': 'GEGU/309Breeze.ipynb', 'repo_name': 'acep-uaf/ACEP_solar', 'size': '2585'}