Update app_backend.py

app_backend.py

@@ -1,153 +1,3 @@
-# import pandas as pd
-# import numpy as np
-# import plotly.express as px
-# from datetime import datetime, timedelta
-# import requests
-
-# # Function to fetch real-time weather data
-# def fetch_weather(api_key, location):
-#     url = f"http://api.openweathermap.org/data/2.5/weather?q={location}&appid={api_key}&units=metric"
-#     response = requests.get(url).json()
-#     if response["cod"] == 200:
-#         return {
-#             "temperature": response["main"]["temp"],
-#             "wind_speed": response["wind"]["speed"],
-#             "weather": response["weather"][0]["description"]
-#         }
-#     return None
-
-# # Generate synthetic grid data
-# def generate_synthetic_data():
-#     time_index = pd.date_range(start=datetime.now(), periods=24, freq="H")
-#     return pd.DataFrame({
-#         "timestamp": time_index,
-#         "total_consumption_kwh": np.random.randint(200, 500, len(time_index)),
-#         "grid_generation_kwh": np.random.randint(150, 400, len(time_index)),
-#         "storage_usage_kwh": np.random.randint(50, 150, len(time_index)),
-#         "solar_output_kw": np.random.randint(50, 150, len(time_index)),
-#         "wind_output_kw": np.random.randint(30, 120, len(time_index)),
-#         "grid_health": np.random.choice(["Good", "Moderate", "Critical"], len(time_index))
-#     })
-
-# # Load optimization recommendation
-# def optimize_load(demand, solar, wind):
-#     renewable_supply = solar + wind
-#     if renewable_supply >= demand:
-#         return "Grid Stable"
-#     return "Use Backup or Adjust Load"
-
-# # Export functions for use in Streamlit
-# if __name__ == "__main__":
-#     print("Backend ready!")
-
-
-
-# code2
-
-
-# import pandas as pd
-# import numpy as np
-# from datetime import datetime, timedelta
-# import requests
-
-# # Function to fetch real-time weather data
-# def fetch_weather(api_key, location):
-#     url = f"http://api.openweathermap.org/data/2.5/weather?q={location}&appid={api_key}&units=metric"
-#     response = requests.get(url).json()
-#     if response["cod"] == 200:
-#         return {
-#             "temperature": response["main"]["temp"],
-#             "wind_speed": response["wind"]["speed"],
-#             "weather": response["weather"][0]["description"]
-#         }
-#     return None
-
-# # Generate synthetic data
-# def generate_synthetic_data():
-#     time_index = pd.date_range(start=datetime.now(), periods=24, freq="H")
-#     return pd.DataFrame({
-#         "timestamp": time_index,
-#         "total_power_consumption_mw": np.random.randint(200, 500, len(time_index)),
-#         "grid_generation_mw": np.random.randint(100, 300, len(time_index)),
-#         "storage_utilization_mw": np.random.randint(50, 150, len(time_index)),
-#     })
-
-# # Generate storage data
-# def generate_storage_data():
-#     return {
-#         "wind": 5,
-#         "solar": 7,
-#         "turbine": 10,
-#         "total_stored_kwh": 2000
-#     }
-
-# # Export functions for use in Streamlit
-# if __name__ == "__main__":
-#     print("Backend ready!")
-
-
-# code 3
-# import pandas as pd
-# import numpy as np
-# from datetime import datetime, timedelta
-
-# # Function to fetch weather data remains unchanged
-
-# # Generate synthetic grid data
-# def generate_synthetic_data():
-#     time_index = pd.date_range(start=datetime.now(), periods=24, freq="H")
-#     return pd.DataFrame({
-#         "timestamp": time_index,
-#         "power_consumption_mw": np.random.randint(50, 200, len(time_index)),
-#         "grid_generation_mw": np.random.randint(30, 150, len(time_index)),
-#         "storage_utilization_mw": np.random.randint(10, 50, len(time_index)),
-#         "grid_health": np.random.choice(["Good", "Moderate", "Critical"], len(time_index))
-#     })
-
-# # Generate synthetic storage data
-# def generate_storage_data():
-#     wind_storage = np.random.randint(5, 15)
-#     solar_storage = np.random.randint(7, 20)
-#     turbine_storage = np.random.randint(10, 25)
-#     total_storage = wind_storage + solar_storage + turbine_storage
-#     return {
-#         "wind_storage_mw": wind_storage,
-#         "solar_storage_mw": solar_storage,
-#         "turbine_storage_mw": turbine_storage,
-#         "total_storage_mw": total_storage
-#     }
-
-# # Generate synthetic trade data
-# def generate_trade_data():
-#     countries = ["Country A", "Country B", "Country C"]
-#     exports = np.random.randint(10, 50, len(countries))
-#     imports = np.random.randint(5, 30, len(countries))
-#     return pd.DataFrame({
-#         "country": countries,
-#         "exports_mw": exports,
-#         "imports_mw": imports
-#     })
-
-# # Updated optimization recommendation
-# def optimize_load(demand, generation, storage):
-#     if generation + storage >= demand:
-#         return "Grid is Stable with Current Supply"
-#     elif demand - (generation + storage) < 20:
-#         return "Activate Backup or Optimize Load"
-#     else:
-#         return "Immediate Action Required: Adjust Load or Increase Generation"
-
-# # Export functions
-# if __name__ == "__main__":
-#     print("Backend ready for enhanced dashboard!")
-
-
-
-
-
-# code 4
-
-
 import pandas as pd
 import numpy as np
 import requests
@@ -185,50 +35,3 @@ def optimize_load(demand, solar, wind):
 
 if __name__ == "__main__":
     print("Backend ready!")
-
-
-
-# code 5
-
-
-# import random
-# import pandas as pd 
-# def fetch_data():
-#     # Simulating fetching data from a database or API
-#     data = {
-#         'temperature': random.uniform(-10, 30),
-#         'wind_speed': random.uniform(0, 20),
-#         'weather_condition': random.choice(['Clear', 'Overcast Clouds', 'Thunderstorm', 'Rain']),
-#         'timestamps': pd.date_range("2025-01-01", periods=10, freq='H'),
-#         'total_consumption': [random.uniform(50, 100) for _ in range(10)],
-#         'grid_generation': [random.uniform(30, 80) for _ in range(10)],
-#         'storage_usage': [random.uniform(10, 30) for _ in range(10)],
-#         'solar_storage': random.uniform(10, 30),
-#         'wind_storage': random.uniform(10, 30),
-#         'hydro_storage': random.uniform(10, 30),
-#         'total_storage': random.uniform(50, 100),
-#     }
-#     return data
-
-# def generate_recommendations(data):
-#     recommendations = []
-#     if data['total_consumption'][-1] > data['grid_generation'][-1]:
-#         recommendations.append("Consider integrating additional renewable sources to meet the current demand.")
-#     if data['storage_usage'][-1] > data['total_storage'] * 0.8:
-#         recommendations.append("Energy storage is running low. Consider optimizing the grid or adding more storage.")
-#     return recommendations
-
-# def grid_health_status(data):
-#     status = "Grid is operating normally."
-#     if data['total_consumption'][-1] > 90:
-#         status = "Warning: High consumption detected!"
-#     if data['wind_speed'] > 15:
-#         status = "Warning: High wind speeds, may affect wind turbine output."
-#     return status
-
-# def generate_trading_options(data):
-#     if data['total_storage'] > 60:
-#         return "Energy is available for export to neighboring countries."
-#     else:
-#         return "Energy reserves are low. Trading is not recommended at this moment."
-