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Classifying energy efficiency of buildings based on Heating and Cooling load
This project evaluates the energy efficiency of buildings using the Energy Efficiency Dataset, sourced from Kaggle (Energy Efficiency Dataset). It employs advanced multi-output classification techniques to predict two target variables—Heating Load (Y1) and Cooling Load (Y2)—based on architectural and physical features of the buildings.
What is the Business problem we are trying to solve with this project?
What is the Business problem we are trying to solve with this project?
How can we optimize energy distribution and provide personalized energy solutions to customers by classifying buildings based on their heating and cooling load demands?
Project Objectives
Project Objectives
Develop a predictive model to classify buildings based on Heating Load and Cooling Load demands.
Identify critical features influencing energy efficiency in buildings.
Provide actionable insights for optimizing energy distribution systems.
Facilitate personalized energy solutions for consumers, such as tailored pricing plans and efficiency upgrades.
Promote sustainability by enabling data-driven decision-making for energy management.
What is Multi-output classifier?
What is Multi-output classifier?
Please check my YouTube video, where I explained the MultiOutput Classifier in detail:
Dataset Details
Dataset Details
Source:Energy Efficiency Dataset
The dataset consists of 768 samples, each representing a unique building configuration. The data is designed to evaluate energy efficiency, particularly heating and cooling loads.
Key Features:
Independent Variables:
Relative Compactness: A measure of how compact the building shape is.
Surface Area: Total exterior surface area of the building (in square units).
Wall Area: Total wall area of the building (in square units).
Roof Area: Total roof area of the building (in square units).
Overall Height: Height of the building (in units).
Orientation: Direction the building faces, coded as integers (e.g., 2, 3, 4, 5).
Glazing Area: Fraction of the overall surface area covered by windows.
Glazing Area Distribution: Distribution pattern of the glazing area, coded as integers.
Target Variable:
Heating Load (energy required for heating)
and Cooling Load (energy required for cooling).
Dataset Summary:
Records: 768 unique configurations with detailed physical and architectural attributes.
Key Methodology
Key Methodology
Step-1: Data Loading and Cleaning:
Loaded the dataset from Kaggle.
Conducted initial data checks to ensure no missing or null values.
Renamed columns for interpretability.
Loaded the dataset from Kaggle.
Conducted initial data checks to ensure no missing or null values.
Step-2 : Exploratory Data Analysis (EDA):
Assessed feature importance and identified significant drivers like Overall Height and Roof Area.
Visualized data distributions and relationships through scatter plots and histograms.
Analyzed correlations using a correlation matrix.
Grouped Heating and Cooling Classes to define.
Step-3 : Data Preprocessing for Mulitiout Classifier:
Split the dataset into Training (80%) and Testing (20%) sets using train_test_split.
Standardized features for uniform scaling using StandardScaler.
Step-4 : Meta Model Training and Evaluation:
Trained models using machine learning techniques with MultiOutputClassifier:
Support Vector Machine (SVM)
Random Forest.
Gradient Boosting.
XGBoost.
Evaluated all meta model performance using:
Precision, Recall, F1-Score.
Confusion Matrices to visualize misclassifications.
Step-5 : Individual Model training, and perfomance evaluation for Heating Load and Cooling Load respectively.
Hyperparameter Tuning:
Fine-tuned model parameters using Bayesian Optimization for XGBoost and other advanced models for respective Output features.
Optimized parameters such as learning rate, max depth, and subsample size to enhance performance.
Performance Evaluation:
Evaluated model performance using:
Precision, Recall, F1-Score.
AUC-ROC for multi-class classification.
Confusion Matrices to visualize misclassifications.
XGBoost achieved the highest accuracy of (~99%) for Heating and (~96%) for Cooling Load classifications.
Step-6: Optimization:
Performed dimensionality reduction while maintaining accuracy.
Adjusted thresholds for better precision-recall balance.
Visualization:
Plotted actual vs predicted classes for Heating and Cooling Load.
Generated scatter plots, heatmaps, and ROC curves for analysis and validation
Why I used MultiOutputClassifier?
Why I used MultiOutputClassifier?
Using MultiOutputClassifier offers several advantages for handling multi-output tasks, especially when managing multiple dependent variables.
Follow are the key advantages:
Dual Output Handling:
MultiOutputClassifier enables the training of separate classifiers for each target variable (Heating Load and Cooling Load) within a unified framework.
Simplicity in Implementation:
Instead of manually training two independent models, MultiOutputClassifier simplifies the process with a single fit() and predict() function for multiple outputs.
Extensibility:
It allows the use of any scikit-learn-compatible estimator, like XGBoost, for multi-output tasks, ensuring flexibility.
However, MultiOutputClassifier cannot be used in the Production ?
However, MultiOutputClassifier cannot be used in the Production ?
Despite its utility during the experimentation phase, MultiOutputClassifier has significant limitations for deployment in production environments:
Separate model instances:
MultiOutputClassifier creates independent models for each output, leading to increased memory consumption and computational overhead, which is less efficient for production-grade systems.
Inconsistent Scalability:
As the number of output variables increases, the model's complexity scales linearly, making it unsuitable for applications with many outputs.
Limited Integration Support:
Many production environments prefer single-model architectures for easier deployment, maintenance, and inference optimization.
Key Insights
Key Insights
Strong Correlation:
Heating Load and Cooling Load exhibit a strong positive correlation, indicating that inefficiencies in insulation or building design often affect both targets similarly.
Feature Impact:
Features like Overall Height and Roof Area were identified as key drivers for both Heating and Cooling Loads, while Orientation showed minimal influence.
Model Performance:
The XGBoost model explained nearly 99% of the variance in heating and cooling classifications, showcasing its effectiveness as a predictor.
Actionable Insight:
Targeting buildings with high heating and cooling demands can lead to significant energy savings and better resource allocation.
Code Workflow
Code Workflow
Libraries Used
Libraries Used
Pandas for data manipulation and preprocessing.
NumPy for numerical operations.
Matplotlib and Seaborn for data visualization.
Scikit-learn for preprocessing, model training, and evaluation.
XGBoost and skopt for advanced model training and hyperparameter tuning.
Data Loading and Cleaning
Data Loading and Cleaning
import pandas as pd
import numpy as np
# Load dataset
df = pd.read_csv('/kaggle/input/eergy-efficiency-dataset/ENB2012_data.csv')
# Check for missing values
print(df.isnull().sum())
# Rename columns for clarity
df.rename(columns={
'X1': 'Relative_Compactness',
'X2': 'Surface_Area',
'X3': 'Wall_Area',
'X4': 'Roof_Area',
'X5': 'Overall_Height',
'X6': 'Orientation',
'X7': 'Glazing_Area',
'X8': 'Glazing_Area_Distribution',
'Y1': 'Heating_Load',
'Y2': 'Cooling_Load'
}, inplace=True)
# Standardize features
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_features = scaler.fit_transform(df.iloc[:, :-2])
Exploratory Data Analysis
Exploratory Data Analysis
import matplotlib.pyplot as plt
import seaborn as sns
# Correlation heatmap
sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
plt.show()
# Distribution plots
sns.histplot(df['Heating_Load'], kde=True)
plt.title("Heating Load Distribution")
plt.show()
sns.scatterplot(data=df, x='Heating_Load', y='Wall_Area', hue='Cooling_Load')
plt.show()
Data Splitting
Data Splitting
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)
Model Training with MultiOutputClassifier
Model Training with MultiOutputClassifier
from sklearn.multioutput import MultiOutputClassifier
from xgboost import XGBClassifier
model = MultiOutputClassifier(XGBClassifier())
model.fit(X_train, y_train)
Performance Metrics
Performance Metrics
from sklearn.metrics import accuracy_score, classification_report
print(classification_report(y_test_heat, y_pred_heat))
print(f"Accuracy score :{accuracy_score(y_test_heat, y_pred_heat)}")
Conclusion
Conclusion
This project demonstrates the potential of multi-output classification in optimizing energy efficiency in buildings. By leveraging the Energy Efficiency Dataset, the findings reveal that features such as Overall height and Roof Area are critical in predicting heating and cooling demands.
The XGBoost model performed exceptionally, explaining nearly 99% of the variance in energy classifications, and provides actionable insights to enhance sustainability and resource management.
This work sets the foundation for integrating data-driven solutions into energy management systems and emphasizes the importance of energy-efficient building designs.
For more details, check my github repository: Click Here
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