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import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
# App Configuration
st.set_page_config(page_title="Energy Optimization AI", layout="wide", page_icon="🌞")
# App Title
st.title("🌞⚡ Energy Optimization AI ⚡🌬️")
st.write("""
Optimize your energy distribution across renewable sources in real-time
to maximize efficiency, reduce costs, and promote sustainability.
""")
# Overview Section
st.header("Project Overview")
st.write("""
This application leverages **AI-based decision-making** to analyze your energy usage across solar and wind energy sources. It provides recommendations for optimizing cost-effectiveness and efficiency while maintaining sustainability goals. Use this tool to make informed energy distribution decisions for homes, industries, or renewable energy setups.
""")
# Input Section
st.header("Input Energy Details")
col1, col2 = st.columns(2)
# User Inputs
with col1:
st.subheader("Solar Energy")
solar_usage = st.slider("Solar Energy Usage (kWh):", 0.0, 1000.0, 200.0, step=10.0)
solar_cost = st.slider("Solar Cost per kWh:", 0.0, 10.0, 2.5, step=0.1)
with col2:
st.subheader("Wind Energy")
wind_usage = st.slider("Wind Energy Usage (kWh):", 0.0, 1000.0, 300.0, step=10.0)
wind_cost = st.slider("Wind Cost per kWh:", 0.0, 10.0, 1.8, step=0.1)
# Optimization Logic
if st.button("🚀 Optimize"):
total_usage = solar_usage + wind_usage
if total_usage == 0:
st.error("⚠️ Please enter energy usage for at least one source.")
else:
total_cost = (solar_usage * solar_cost) + (wind_usage * wind_cost)
solar_share = (solar_usage / total_usage) * 100 if solar_usage > 0 else 0
wind_share = (wind_usage / total_usage) * 100 if wind_usage > 0 else 0
cost_effective_source = (
"solar" if solar_cost < wind_cost
else "wind" if wind_cost < solar_cost
else "both sources equally"
)
suggestion = (
f"Balance usage between solar and wind energy, prioritizing {cost_effective_source} for lower costs."
)
# Display Results
st.subheader("Optimization Results")
col3, col4 = st.columns(2)
with col3:
st.metric("Total Energy Usage", f"{total_usage:.2f} kWh")
st.metric("Solar Share", f"{solar_share:.2f}%")
with col4:
st.metric("Total Cost", f"{total_cost:.2f} currency")
st.metric("Wind Share", f"{wind_share:.2f}%")
st.success(suggestion)
# Charts
st.subheader("Visualization")
chart_data = pd.DataFrame(
{
"Energy Source": ["Solar", "Wind"],
"Usage (kWh)": [solar_usage, wind_usage],
"Cost (Currency)": [solar_usage * solar_cost, wind_usage * wind_cost],
}
)
# Improved Visualization with Plotly
fig = go.Figure()
# Add Usage Bar
fig.add_trace(go.Bar(
x=chart_data["Energy Source"],
y=chart_data["Usage (kWh)"],
name="Usage (kWh)",
marker_color='blue'
))
# Add Cost Bar
fig.add_trace(go.Bar(
x=chart_data["Energy Source"],
y=chart_data["Cost (Currency)"],
name="Cost (Currency)",
marker_color='green'
))
fig.update_layout(
barmode='group',
title="Energy Usage and Cost Comparison",
xaxis_title="Energy Source",
yaxis_title="Value",
legend_title="Metrics",
)
st.plotly_chart(fig, use_container_width=True)
# Explanation
st.subheader("Detailed Explanation")
st.write(
f"Based on your inputs, **{cost_effective_source} energy** is more cost-effective. "
f"This analysis helps balance your energy sources to minimize costs and maximize efficiency."
)
# Sidebar
st.sidebar.header("About")
st.sidebar.info("""
This tool helps optimize renewable energy usage for cost savings, sustainability,
and better energy distribution management.
""")
st.sidebar.header("How It Works")
st.sidebar.write("""
1. **Input Energy Details**: Specify the usage and cost for solar and wind energy.
2. **Optimization**: The tool calculates the total energy usage, cost, and shares of each source.
3. **Visual Analysis**: Get insights into cost-effective strategies with detailed charts and recommendations.
""")
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