| import streamlit as st |
| import pandas as pd |
| import numpy as np |
| import os |
| import joblib |
|
|
| |
| st.set_page_config(page_title="SunStream Predictor", layout="wide") |
|
|
| st.markdown(""" |
| <style> |
| .main { background-color: #f5f7f9; } |
| .stMetric { background-color: #ffffff; padding: 15px; border-radius: 10px; box-shadow: 0 2px 4px rgba(0,0,0,0.05); } |
| </style> |
| """, unsafe_allow_html=True) |
|
|
|
|
|
|
| BASE_DIR = os.path.dirname(__file__) |
|
|
| model_path = os.path.join(BASE_DIR, "solar_model.pkl") |
| scaler_path = os.path.join(BASE_DIR, "solar_scaler.pkl") |
|
|
| |
| @st.cache_resource |
| def load_assets(): |
| model = joblib.load(model_path) |
| scaler = joblib.load(scaler_path) |
| return model, scaler |
|
|
| try: |
| model, scaler = load_assets() |
| except Exception as e: |
| st.error(f"Error loading model: {e}") |
| st.stop() |
|
|
| |
| st.title("☀️ SunStream: Solar Output Predictor") |
| st.markdown("Professional-grade energy forecasting based on atmospheric conditions.") |
| st.divider() |
|
|
| |
| st.sidebar.header("Control Panel") |
| st.sidebar.info("Adjust weather parameters to see real-time predictions.") |
|
|
| hour = st.sidebar.slider("Hour of Day (24h)", 0, 23, 12) |
| cloud_cover = st.sidebar.slider("Cloud Cover (0-1)", 0.0, 1.0, 0.2, 0.01) |
| temp = st.sidebar.slider("Temperature (°C)", -10.0, 45.0, 25.0) |
| humidity = st.sidebar.slider("Relative Humidity (%)", 0.0, 100.0, 50.0) |
| visibility = st.sidebar.number_input("Visibility (km)", value=10.0) |
| wind_speed = st.sidebar.number_input("Wind Speed", value=5.0) |
| pressure = st.sidebar.number_input("Pressure", value=29.2) |
| altimeter = st.sidebar.number_input("Altimeter", value=30.0) |
| dew_point = st.sidebar.number_input("Dew Point", value=10.0) |
|
|
|
|
| |
| def predict(): |
| |
| h_sin = np.sin(2 * np.pi * hour / 23.0) |
| h_cos = np.cos(2 * np.pi * hour / 23.0) |
| heat_idx = temp * humidity |
|
|
| |
| features = np.array([[ |
| cloud_cover, visibility, temp, dew_point, |
| humidity, wind_speed, pressure, altimeter, |
| h_sin, h_cos, heat_idx |
| ]]) |
|
|
| |
| features_scaled = scaler.transform(features) |
| prediction = model.predict(features_scaled)[0] |
| return max(0, round(prediction, 2)) |
|
|
|
|
| result = predict() |
|
|
| |
| col1, col2, col3 = st.columns([1, 1, 1]) |
|
|
| with col1: |
| st.metric(label="Estimated Output", value=f"{result} kWh") |
|
|
| with col2: |
| efficiency = min(100, (result / 4500) * 100) |
| st.metric(label="System Efficiency", value=f"{efficiency:.1f}%") |
|
|
| with col3: |
| status = "Optimal" if cloud_cover < 0.3 and result > 1000 else "Sub-optimal" |
| st.write(f"Current Status: \n## {status}") |
|
|
| |
| st.subheader("Hourly Forecast Simulation") |
| st.caption("How your current weather conditions would perform throughout the day.") |
|
|
|
|
| hours = np.arange(0, 24) |
| curve = [] |
| for h in hours: |
| h_sin, h_cos = np.sin(2 * np.pi * h / 23.0), np.cos(2 * np.pi * h / 23.0) |
| feat = np.array([[cloud_cover, visibility, temp, dew_point, humidity, wind_speed, pressure, altimeter, h_sin, h_cos, |
| temp * humidity]]) |
| scaled_feat = scaler.transform(feat) |
| curve.append(max(0, model.predict(scaled_feat)[0])) |
|
|
| chart_data = pd.DataFrame({"Hour": hours, "kWh": curve}) |
| st.area_chart(chart_data.set_index("Hour"), color="#FF8C00") |
|
|
| st.success(" Innovation Tip: This model uses Cyclical Encoding to understand that 23:00 is close to 00:00!") |
