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| # 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", "Electricity Storage", "Electricity 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) | |
| # code 2 | |
| # import streamlit as st | |
| # import pandas as pd | |
| # import plotly.graph_objects as go | |
| # from app_backend import fetch_weather, generate_synthetic_data, generate_storage_data | |
| # # Constants | |
| # API_KEY = "84e26811a314599e940f343b4d5894a7" | |
| # DEFAULT_LOCATION = "Pakistan" | |
| # # Sidebar for location and weather data | |
| # st.sidebar.title("Smart Grid Dashboard") | |
| # location = st.sidebar.text_input("Enter Location", DEFAULT_LOCATION) | |
| # 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 interface | |
| # st.title("Real-Time Smart Grid Dashboard") | |
| # # Tabs | |
| # tabs = st.tabs(["Home", "Power Storage", "Electricity Trade Management"]) | |
| # # Home Tab | |
| # with tabs[0]: | |
| # st.header("Overview: Power and Energy Usage") | |
| # # Fetch synthetic data | |
| # data = generate_synthetic_data() | |
| # # Line Graph for Power Consumption, Generation, and Storage | |
| # fig = go.Figure() | |
| # fig.add_trace(go.Scatter( | |
| # x=data["timestamp"], | |
| # y=data["total_power_consumption_mw"], | |
| # mode='lines', | |
| # name="Total Power Consumption (MW)", | |
| # line=dict(color="red") | |
| # )) | |
| # fig.add_trace(go.Scatter( | |
| # x=data["timestamp"], | |
| # y=data["grid_generation_mw"], | |
| # mode='lines', | |
| # name="Grid Generation (MW)", | |
| # line=dict(color="green") | |
| # )) | |
| # fig.add_trace(go.Scatter( | |
| # x=data["timestamp"], | |
| # y=data["storage_utilization_mw"], | |
| # mode='lines', | |
| # name="Storage Utilization (MW)", | |
| # line=dict(color="blue") | |
| # )) | |
| # fig.update_layout(title="Power and Energy Trends", xaxis_title="Time", yaxis_title="Power (MW)") | |
| # st.plotly_chart(fig) | |
| # # Storage Tab | |
| # with tabs[1]: | |
| # st.header("Energy Storage Overview") | |
| # storage_data = generate_storage_data() | |
| # st.write(f"**Total Energy Stored:** {storage_data['total_stored_kwh']} kWh") | |
| # # Circular storage breakdown | |
| # sources = ["Wind", "Solar", "Turbine"] | |
| # values = [storage_data["wind"], storage_data["solar"], storage_data["turbine"]] | |
| # fig = go.Figure(data=[go.Pie(labels=sources, values=values, hole=.4)]) | |
| # fig.update_layout(title="Energy Storage Breakdown") | |
| # st.plotly_chart(fig) | |
| # # Electricity Trade Management Tab | |
| # with tabs[2]: | |
| # st.header("Electricity Trade Management") | |
| # # Sample trade data | |
| # trade_data = { | |
| # "Country": ["Srilanka", "China", "Bangladesh"], | |
| # "Energy Exported (MW)": [50, 30, 70], | |
| # "Energy Imported (MW)": [20, 40, 10], | |
| # } | |
| # trade_df = pd.DataFrame(trade_data) | |
| # st.subheader("Trade Details") | |
| # st.write(trade_df) | |
| # # Visualization | |
| # fig = go.Figure() | |
| # fig.add_trace(go.Bar(x=trade_df["Country"], y=trade_df["Energy Exported (MW)"], name="Exported", marker_color='purple')) | |
| # fig.add_trace(go.Bar(x=trade_df["Country"], y=trade_df["Energy Imported (MW)"], name="Imported", marker_color='orange')) | |
| # fig.update_layout(title="Energy Trade", barmode='group') | |
| # st.plotly_chart(fig) | |
| # code 3 | |
| import streamlit as st | |
| import pandas as pd | |
| import plotly.graph_objects as go | |
| from app_backend import fetch_weather, generate_synthetic_data, generate_storage_data | |
| # Constants | |
| API_KEY = "84e26811a314599e940f343b4d5894a7" | |
| DEFAULT_LOCATION = "pakistan" | |
| # Sidebar for location and weather data | |
| st.sidebar.title("Smart Grid Dashboard") | |
| location = st.sidebar.text_input("Enter Location", DEFAULT_LOCATION) | |
| 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 interface | |
| st.title("Real-Time Smart Grid Dashboard") | |
| # Tabs | |
| tabs = st.tabs(["Home", "Storage", "Electricity Trade Management"]) | |
| # Home Tab | |
| with tabs[0]: | |
| st.header("Overview: Power and Energy Usage") | |
| # Fetch synthetic data | |
| data = generate_synthetic_data() | |
| # Line Graph for Power Consumption, Generation, and Storage | |
| fig = go.Figure() | |
| fig.add_trace(go.Scatter( | |
| x=data["timestamp"], | |
| y=data["total_power_consumption_mw"], | |
| mode='lines', | |
| name="Total Power Consumption (MW)", | |
| line=dict(color="red") | |
| )) | |
| fig.add_trace(go.Scatter( | |
| x=data["timestamp"], | |
| y=data["grid_generation_mw"], | |
| mode='lines', | |
| name="Grid Generation (MW)", | |
| line=dict(color="green") | |
| )) | |
| fig.add_trace(go.Scatter( | |
| x=data["timestamp"], | |
| y=data["storage_utilization_mw"], | |
| mode='lines', | |
| name="Storage Utilization (MW)", | |
| line=dict(color="blue") | |
| )) | |
| fig.update_layout(title="Power and Energy Trends", xaxis_title="Time", yaxis_title="Power (MW)") | |
| st.plotly_chart(fig) | |
| # Grid Health Indicator | |
| st.subheader("Grid Health Status") | |
| grid_health = "Stable" if data["grid_generation_mw"].mean() >= data["total_power_consumption_mw"].mean() else "Critical" | |
| st.write(f"**Grid Health:** {grid_health}") | |
| # AI Recommendations | |
| st.subheader("AI Recommendations") | |
| recommendations = [ | |
| "Increase solar panel efficiency by 10% for peak hours.", | |
| "Optimize wind turbine alignment based on real-time wind data.", | |
| "Store excess energy during low-demand periods for future use.", | |
| "Improve grid stability by distributing load dynamically across sectors.", | |
| ] | |
| for rec in recommendations: | |
| st.write(f"- {rec}") | |
| # Storage Tab | |
| with tabs[1]: | |
| st.header("Energy Storage Overview") | |
| storage_data = generate_storage_data() | |
| # Individual Circles for Wind, Solar, and Turbine | |
| st.subheader("Energy Contributions") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric("Wind Energy", f"{storage_data['wind']} MW/min") | |
| with col2: | |
| st.metric("Solar Energy", f"{storage_data['solar']} MW/min") | |
| with col3: | |
| st.metric("Turbine Energy", f"{storage_data['turbine']} MW/min") | |
| # Central Grid Storage Visualization | |
| st.subheader("Total Energy Stored in Grid") | |
| fig = go.Figure() | |
| fig.add_trace(go.Scatter(x=[0], y=[0], mode='markers+text', text=["Grid"], marker=dict(size=70, color="blue"))) | |
| fig.add_trace(go.Scatter( | |
| x=[-1, 1, 0], | |
| y=[1, 1, -1], | |
| mode='markers+text', | |
| text=["Wind", "Solar", "Turbine"], | |
| marker=dict(size=50, color=["green", "yellow", "orange"]) | |
| )) | |
| fig.add_trace(go.Scatter( | |
| x=[-0.5, 0.5, 0], | |
| y=[0.5, 0.5, -0.5], | |
| mode="lines", | |
| line=dict(width=3, color="gray"), | |
| )) | |
| fig.update_layout( | |
| title="Energy Storage Visualization", | |
| xaxis=dict(visible=False), | |
| yaxis=dict(visible=False), | |
| showlegend=False | |
| ) | |
| st.plotly_chart(fig) | |
| st.write(f"**Total Energy Stored:** {storage_data['total_stored_kwh']} kWh") | |
| # Electricity Trade Management Tab | |
| with tabs[2]: | |
| st.header("Electricity Trade Management") | |
| # Sample trade data | |
| trade_data = { | |
| "Country": ["Country A", "Country B", "Country C"], | |
| "Energy Exported (MW)": [50, 30, 70], | |
| "Energy Imported (MW)": [20, 40, 10], | |
| } | |
| trade_df = pd.DataFrame(trade_data) | |
| st.subheader("Trade Details") | |
| st.write(trade_df) | |
| # Visualization | |
| fig = go.Figure() | |
| fig.add_trace(go.Bar(x=trade_df["Country"], y=trade_df["Energy Exported (MW)"], name="Exported", marker_color='purple')) | |
| fig.add_trace(go.Bar(x=trade_df["Country"], y=trade_df["Energy Imported (MW)"], name="Imported", marker_color='orange')) | |
| fig.update_layout(title="Energy Trade", barmode='group') | |
| st.plotly_chart(fig) | |