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# 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!")
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 in MW (for generation) and kWh (for load)
def generate_synthetic_data():
time_index = pd.date_range(start=datetime.now(), periods=24, freq="H")
return pd.DataFrame({
"timestamp": time_index,
"load_demand_mw": np.random.uniform(0.2, 0.5, len(time_index)), # Load demand in MW
"solar_output_mw": np.random.uniform(0.05, 0.15, len(time_index)), # Solar output in MW
"wind_output_mw": np.random.uniform(0.03, 0.12, len(time_index)), # Wind output in MW
"battery_storage_kwh": np.random.randint(100, 500, len(time_index)), # Battery storage in kWh
"grid_health": np.random.choice(["Good", "Moderate", "Critical"], len(time_index))
})
# Load optimization recommendation in MW
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!") |