# 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 from datetime import datetime # 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, "load_demand_kwh": np.random.randint(200, 500, 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" 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."