app.py

import numpy as np
import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from keras.models import load_model
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

# Load the data
data = pd.read_csv('Solar_Irradiance.csv')
data['Latitude'] = data['Latitude'].str.rstrip('°').astype(float)
data['Longitude'] = data['Longitude'].str.rstrip('°').astype(float)

# Extract features and target
features = data[['Month', 'Hour', 'Latitude', 'Longitude', 'Panel_Capacity(W)', 'Panel_Efficiency', 'Wind_Speed(km/h)', 'Cloud_Cover(%)', 'temperature (°f)']]
target = data['Irradiance(W/m^2)']

# Set up the encoder and scaler
encoder = OneHotEncoder(sparse=False, categories='auto')
categorical_features = features[['Month', 'Hour']]
encoder.fit(categorical_features)
scaler = StandardScaler()
scaler.fit(features[['Latitude', 'Longitude', 'Panel_Capacity(W)', 'Panel_Efficiency', 'Wind_Speed(km/h)', 'Cloud_Cover(%)', 'temperature (°f)']])

# Load the saved model
loaded_model = load_model('solar_irradiance_model.h5')

# Streamlit app setup
st.title("Solar Irradiance Prediction")
st.sidebar.header("Input Parameters")

# User inputs from sidebar
month = st.sidebar.selectbox("Month", data['Month'].unique())
hour = st.sidebar.slider("Hour", 0, 23, 12)
latitude = st.sidebar.number_input("Latitude", value=28.570633)
longitude = st.sidebar.number_input("Longitude", value=77.327215)
panel_capacity = st.sidebar.number_input("Panel Capacity (W)", value=500)
panel_efficiency = st.sidebar.number_input("Panel Efficiency", value=0.15)
wind_speed = st.sidebar.number_input("Wind Speed (km/h)", value=6.43988)
cloud_cover = st.sidebar.number_input("Cloud Cover (%)", value=17.7)
temperature = st.sidebar.number_input("Temperature (°F)", value=55.0)

# Function to predict irradiance
def predict_irradiance(month, hour, latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature):
    encoded_month_hour = encoder.transform([[month, hour]])
    scaled_features = scaler.transform([[latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature]])
    processed_features = np.concatenate((encoded_month_hour, scaled_features), axis=1)
    reshaped_features = np.reshape(processed_features, (1, 1, processed_features.shape[1]))
    predicted_irradiance = loaded_model.predict(reshaped_features)
    return max(predicted_irradiance[0][0], 0.0)

# Display the predicted irradiance
predicted_irradiance = predict_irradiance(month, hour, latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature)
st.write(f"Predicted irradiance for {month}, hour {hour}: {predicted_irradiance:.2f} W/m^2")

# Plot actual vs. predicted irradiance
def plot_actual_vs_predicted(month):
    actual_irradiance = data[data['Month'] == month]['Irradiance(W/m^2)'].values
    predicted_irradiances = []

    for hour in range(24):
        irradiance = predict_irradiance(month, hour, latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature)
        predicted_irradiances.append(irradiance)

    plt.figure(figsize=(12, 6))
    plt.plot(range(24), actual_irradiance, label='Actual Irradiance', color='blue')
    plt.plot(range(24), predicted_irradiances, label='Predicted Irradiance', color='red')
    plt.xlabel('Hour')
    plt.ylabel('Irradiance (W/m^2)')
    plt.title(f'Actual vs. Predicted Irradiance for {month}')
    plt.legend()
    st.pyplot(plt)

# Display the plot
plot_actual_vs_predicted(month)

# Function to calculate and display evaluation metrics
def display_evaluation_metrics():
    metrics_df = pd.DataFrame(columns=['Month', 'MSE', 'RMSE', 'MAE', 'R-squared'])

    for month in data['Month'].unique():
        actual_irradiance = get_actual_irradiance(month)
        predicted_irradiance = []

        for hour in range(24):
            irradiance = predict_irradiance(month, hour, latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature)
            predicted_irradiance.append(irradiance)

        mse = mean_squared_error(actual_irradiance, predicted_irradiance)
        rmse = np.sqrt(mse)
        mae = mean_absolute_error(actual_irradiance, predicted_irradiance)
        r2 = r2_score(actual_irradiance, predicted_irradiance)

        metrics_df = metrics_df.append({'Month': month, 'MSE': mse, 'RMSE': rmse, 'MAE': mae, 'R-squared': r2}, ignore_index=True)

    st.write("Evaluation Metrics for Each Month")
    st.dataframe(metrics_df)

# Display evaluation metrics
display_evaluation_metrics()

# Function to plot actual vs. predicted irradiance scatter plot
def plot_irradiance_scatter(month):
    actual_irradiance = get_actual_irradiance(month)
    predicted_irradiances = []

    for hour in range(24):
        irradiance = predict_irradiance(month, hour, latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature)
        predicted_irradiances.append(irradiance)

    plt.figure(figsize=(8, 6))
    plt.scatter(range(24), actual_irradiance, label='Actual Irradiance', color='blue')
    plt.scatter(range(24), predicted_irradiances, label='Predicted Irradiance', color='red')
    plt.xlabel('Hour')
    plt.ylabel('Irradiance (W/m^2)')
    plt.title(f'Actual vs. Predicted Irradiance for {month}')
    plt.legend()
    st.pyplot(plt)

# Example usage: scatter plot for selected month
plot_irradiance_scatter(month)

# Function to plot hour vs. irradiance for all months
def plot_hour_vs_irradiance():
    months = data['Month'].unique()
    hour_range = range(24)

    predicted_irradiances = np.zeros((len(months), 24))
    actual_irradiances = np.zeros((len(months), 24))

    for i, month in enumerate(months):
        for hour in hour_range:
            if hour in range(6) or hour in range(18, 24):
                irradiance = 0  # Set predicted irradiance to 0
            else:
                irradiance = predict_irradiance(month, hour, latitude, longitude, panel_capacity, panel_efficiency, wind_speed, cloud_cover, temperature)
            predicted_irradiances[i][hour] = irradiance
            actual_irradiances[i][hour] = get_actual_irradiance(month)[hour]

    bar_width = 0.35
    index = np.arange(len(hour_range))

    plt.figure(figsize=(12, 6))
    plt.bar(index, predicted_irradiances.mean(axis=0), bar_width, label='Predicted Irradiance')
    plt.bar(index + bar_width, actual_irradiances.mean(axis=0), bar_width, label='Actual Irradiance')

    plt.xlabel('Hour')
    plt.ylabel('Irradiance (W/m^2)')
    plt.title('Hour vs. Irradiance (Average for All Months)')
    plt.xticks(index + bar_width/2, hour_range)
    plt.legend()
    st.pyplot(plt)

# Plot hour vs. irradiance for all months
plot_hour_vs_irradiance()