# import streamlit as st # import pandas as pd # import plotly.express as px # from app_backend import fetch_weather, generate_synthetic_data, optimize_load # # Constants # API_KEY = "84e26811a314599e940f343b4d5894a7" # LOCATION = "Pakistan" # # Sidebar # st.sidebar.title("Smart Grid Dashboard") # location = st.sidebar.text_input("Enter Location", LOCATION) # # Fetch and display weather data # weather = fetch_weather(API_KEY, location) # if weather: # st.sidebar.write(f"Temperature: {weather['temperature']} °C") # st.sidebar.write(f"Wind Speed: {weather['wind_speed']} m/s") # st.sidebar.write(f"Weather: {weather['weather']}") # # Main dashboard with tabs # tabs = st.tabs(["Home", "Storage", "Trading"]) # with tabs[0]: # st.title("Real-Time Smart Grid Dashboard") # # Generate synthetic data # data = generate_synthetic_data() # # Plot total consumption, grid generation, and storage usage # fig = px.line(data, x="timestamp", y=["total_consumption_kwh", "grid_generation_kwh", "storage_usage_kwh"], # title="Energy Consumption, Generation, and Storage Usage Over Time", # labels={"value": "Energy (kWh)", "variable": "Energy Source"}) # st.plotly_chart(fig) # # Grid health overview # st.subheader("Grid Health Overview") # grid_health_counts = data["grid_health"].value_counts() # st.bar_chart(grid_health_counts) # # Optimization recommendations # current_demand = data["total_consumption_kwh"].iloc[-1] # current_solar = data["solar_output_kw"].iloc[-1] # current_wind = data["wind_output_kw"].iloc[-1] # recommendation = optimize_load(current_demand, current_solar, current_wind) # st.subheader("Recommendations") # st.write(f"Current Load Demand: {current_demand} kWh") # st.write(f"Solar Output: {current_solar} kW") # st.write(f"Wind Output: {current_wind} kW") # st.write(f"Recommendation: {recommendation}") # with tabs[1]: # st.title("Energy Storage Overview") # # Total energy stored # total_storage = 500 # Example of total energy storage # st.subheader(f"Total Energy Stored: {total_storage} kWh") # # Energy storage contribution from different sources # st.subheader("Energy Storage Contributions") # energy_sources = pd.DataFrame({ # "Source": ["Wind", "Solar", "Turbine"], # "Energy (kW/min)": [5, 7, 10] # }) # st.bar_chart(energy_sources.set_index("Source")) # # Show energy storage status with a rounded circle # st.subheader("Energy Storage Circle") # st.markdown("Energy storage is a combination of contributions from different renewable sources.") # # Visualization of energy storage circle using Plotly # storage_data = { # "Source": ["Wind", "Solar", "Turbine"], # "Energy": [5, 7, 10], # } # storage_df = pd.DataFrame(storage_data) # fig = px.pie(storage_df, names="Source", values="Energy", title="Energy Storage Sources") # st.plotly_chart(fig) # with tabs[2]: # st.title("Energy Trading Overview") # # Energy cubes # st.subheader("Energy Cubes Stored") # energy_cubes = pd.DataFrame({ # "Country": ["China", "Sri Lanka", "Bangladesh"], # "Energy (kWh)": [100, 200, 300], # "Shareable": [True, True, False] # }) # # Displaying the energy cubes in a grid # st.write("Stored energy can be shared with other countries.") # st.dataframe(energy_cubes) # # Visualization of energy that can be shared # st.subheader("Energy Trading Visualization") # st.markdown("The following energy amounts are available for sharing with different countries.") # trading_fig = px.bar(energy_cubes, x="Country", y="Energy (kWh)", color="Shareable", title="Energy Trading") # st.plotly_chart(trading_fig) import streamlit as st import pandas as pd import plotly.express as px from app_backend import fetch_weather, generate_synthetic_data, optimize_load # Constants API_KEY = "84e26811a314599e940f343b4d5894a7" LOCATION = "Pakistan" # Sidebar st.sidebar.title("Smart Grid Dashboard") location = st.sidebar.text_input("Enter Location", LOCATION) # Fetch and display weather data weather = fetch_weather(API_KEY, location) if weather: st.sidebar.write(f"Temperature: {weather['temperature']} °C") st.sidebar.write(f"Wind Speed: {weather['wind_speed']} m/s") st.sidebar.write(f"Weather: {weather['weather']}") # Main dashboard st.title("Real-Time Smart Grid Dashboard") # Generate synthetic data data = generate_synthetic_data() # Plot total power consumption (load demand) in MW fig = px.line(data, x="timestamp", y="load_demand_mw", title="Power Consumption (MW) Over Time") st.plotly_chart(fig) # Plot renewable energy generation in MW (solar + wind) on the graph fig = px.bar( data, x="timestamp", y=["solar_output_mw", "wind_output_mw"], title="Renewable Energy Generation (MW)", labels={"value": "Power (MW)", "variable": "Energy Source"} ) st.plotly_chart(fig) # Show battery storage in kWh fig = px.line(data, x="timestamp", y="battery_storage_kwh", title="Battery Storage (kWh) Over Time") st.plotly_chart(fig) # Grid health st.subheader("Grid Health Overview") grid_health_counts = data["grid_health"].value_counts() st.bar_chart(grid_health_counts) # Optimization recommendations current_demand = data["load_demand_mw"].iloc[-1] # Load demand in MW current_solar = data["solar_output_mw"].iloc[-1] # Solar output in MW current_wind = data["wind_output_mw"].iloc[-1] # Wind output in MW recommendation = optimize_load(current_demand, current_solar, current_wind) st.subheader("Recommendations") st.write(f"Current Load Demand: {current_demand} MW") st.write(f"Solar Output: {current_solar} MW") st.write(f"Wind Output: {current_wind} MW") st.write(f"Recommendation: {recommendation}") # Electricity Trade Management Tab st.subheader("Electricity Trade Management") st.write("Manage energy trading here.")