visualize_application_of_model.py

# -*- coding: utf-8 -*-
"""
Created on Sun Jan 26 22:06:19 2025

@author: luisfernando
"""

import numpy as np
import pandas as pd
from joblib import load
import matplotlib.pyplot as plt
import time

'''
Start the process below.
'''
START_PROCESS = time.time()

# Constants and fixed features for a colder day
indoor_temp = 22  # Indoor temperature in °C
outdoor_temp_base = -15  # Base outdoor temperature for a colder day
amplitude = 5  # Smaller daily temperature amplitude for a colder day
time_intervals = np.arange(0, 24, 0.25)  # 96 intervals (every 15 minutes)

# Generate `Diff` as the difference between indoor and outdoor temperatures
outdoor_temp = outdoor_temp_base + amplitude * (1 - np.cos(2 * np.pi * time_intervals / 24))
Diff = indoor_temp - outdoor_temp

# Generate the dataset for 96 intervals with adjusted feature values
data = pd.DataFrame({
    'Diff': Diff,
    'sqft': [1500] * 96,  # Example fixed value
    'representative_income': [50000] * 96,  # Example fixed value
    'occupants': [4] * 96,  # Example fixed value
    'usage_level': [1.9] * 96,  # Closer to mean (1.9999185)
    'rmb_heating_primary': [57.8] * 96,  # Closer to mean (57.7726467)
    'rmb_heating_backup': [0.0] * 96,  # Closer to mean (0.0)
    'vintage': [1980] * 96,  # Set vintage to 1980
    'rmb_window_area': [193] * 96,  # Closer to mean (193.216062)
    'duct_leakage_and_insulation': [18.4] * 96,  # Closer to mean (18.3767955)
    'heating_setpoint': [69.2] * 96,  # Closer to mean (69.1950475)
    'hvac_heating_efficiency': [83.0] * 96  # Closer to mean (83.0066855)
})

# Hard-coded scaler parameters aligned with the feature list
scaler_mean = [
    22.1731321,  # Diff
    2268.69605,  # sqft
    115660.286,  # representative_income
    2.78307,     # occupants
    1.9999185,   # usage_level
    57.7726467,  # rmb_heating_primary
    0.0,         # rmb_heating_backup
    1986.69608,  # vintage
    193.216062,  # rmb_window_area
    18.3767955,  # duct_leakage_and_insulation
    69.1950475,  # heating_setpoint
    83.0066855   # hvac_heating_efficiency
]
scaler_scale = [
    7.56469631,   # Diff
    1095.20538,   # sqft
    78494.1249,   # representative_income
    1.45644786,   # occupants
    0.704425648,  # usage_level
    28.8588336,   # rmb_heating_primary
    1.0,          # rmb_heating_backup
    12.7390631,   # vintage
    123.946053,   # rmb_window_area
    24.5610850,   # duct_leakage_and_insulation
    2.93397699,   # heating_setpoint
    7.45687177    # hvac_heating_efficiency
]

# Scale the data
scaled_data = (
    (data - scaler_mean) / scaler_scale
).to_numpy()

# Load your model
model_path = "MODELS_PACKED/saved_models_4/Random Forest_Set 4_Main Heat.joblib"
model = load(model_path)

# Predict using the model
predictions = model.predict(scaled_data)

# Add predictions to the dataset
data['Predicted Output'] = predictions

# Plot the predictions and temperature (Diff)
fig, ax1 = plt.subplots(figsize=(12, 6))

# Plot the Predicted Output on the primary y-axis
ax1.plot(time_intervals, predictions, label='Predicted Output', color='tab:blue', marker='o')
ax1.set_xlabel('Time of Day (Hours)', fontsize=14)
ax1.set_ylabel('Predicted Output', color='tab:blue', fontsize=14)
ax1.tick_params(axis='y', labelcolor='tab:blue')
ax1.grid(True, linestyle='--', alpha=0.7)

# Create a secondary y-axis for Diff
ax2 = ax1.twinx()
ax2.plot(time_intervals, Diff, label='Temperature Difference (Diff)', color='tab:red', linestyle='--')
ax2.set_ylabel('Temperature Difference (°C)', color='tab:red', fontsize=14)
ax2.tick_params(axis='y', labelcolor='tab:red')

# Add a legend
fig.legend(loc="upper left", bbox_to_anchor=(0.1, 0.9), fontsize=12)

# Enhance layout
plt.title('Daily Curve Predictions with Temperature Difference', fontsize=16)
plt.xticks(np.arange(0, 25, 2), fontsize=12)
plt.tight_layout()
plt.show()


END_PROCESS = time.time()
TIME_ELAPSED = -START_PROCESS + END_PROCESS
print('\n TIME ELAPSED:')
print(str(TIME_ELAPSED) + ' seconds /', str(TIME_ELAPSED/60) + ' minutes.')