Spaces:
Running
Running
| # 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!") |