A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings
Abstract
1. Introduction
- To identify and quantify the key house features across traditional and modern models that significantly impact energy efficiency;
- To rigorously evaluate the predictive performance and scalability of traditional and modern models in the context of large datasets;
- To assess the robustness of models and address any biases that can affect the analysis.
2. Related Work
3. Methodology
3.1. Description of Dataset
3.2. Feature Transformation
3.2.1. Handling Missing Data
3.2.2. Handling Duplicates
3.2.3. Normalisation
3.2.4. Standardisation
3.2.5. Encoding Categorical Variables
3.3. Features Classification
3.3.1. Building Components
3.3.2. Energy
3.3.3. Environmental Factors
3.3.4. Cost
3.4. Features Selection Process
3.5. Feature Analysis Techniques
3.6. Principal Component Analysis (PCA) Algorithm
Mathematical Model
3.7. Correlation Analysis
Mathematical Model
- and are the sample points for the individual;
- and are the means;
- n is the number of observations.
3.8. Mutual Information Algorithm
Mathematical Model
- is the joint probability distribution function of X and Y;
- and are the marginal probability distribution functions of X and Y, respectively.
3.9. Recursive Feature Elimination (RFE)
Mathematical Model
3.10. Least Absolute Shrinkage and Selection Operator (LASSO)
Mathematical Model
- is the dependent variable;
- are the independent variables;
- is the intercept;
- are the coefficients of the model;
- n is the number of observations;
- p is the number of features;
- is the regularisation parameter.
3.11. Random Forest Algorithm
Mathematical Model
3.12. Gradient Boosting
Mathematical Model
- is the forecast from the -th iteration;
- is the m-th Decision Tree;
- is the learning rate, which helps in applying the regularisation parameter, scaling the contribution of each tree.
3.13. Model Selection
3.14. Linear Regression
Mathematical Model
- Y is the dependent variate;
- are the independent variables;
- is the constant. are the coefficients of the error term, .
3.15. k-Nearest Neighbors (KNNs) Algorithm
Mathematical Model
3.16. Decision Tree
Mathematical Model
3.17. Support Vector Regression (SVR)
Mathematical Model
3.18. Random Forest Algorithm with Extra Trees
3.19. XGBoost and LightGBM
3.20. Hyperparameters Tuning
3.21. Model Evaluation
3.21.1. Mean Squared Error (MSE)
3.21.2. Mean Absolute Error (MAE)
3.21.3. Root Mean Squared Error (RMSE)
3.21.4. R-Squared (R2)
3.21.5. Mean Absolute Percentage Error (MAPE)
3.21.6. Adjusted R-Squared (Adjusted R2)
3.22. Cross-Validation
4. Results
4.1. Key Features
Analysis of Key Feature Contributions
4.2. Model Performance
4.2.1. Performance Metrics
4.2.2. Comparison of Model Performance
4.2.3. Model Performance Discussion
5. Implications of Study
5.1. Theoretical
5.2. Policy
6. Conclusions
6.1. Limitations
6.2. Future Research
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
PCA | Principal component analysis |
KNNs | k-Nearest Neighbours |
SVR | Support Vector Regression |
RF | Random Forest |
GB | Gradient Boosting |
XGBoost | Extreme Gradient Boosting |
LightGBM | Light Gradient Boosting Machine |
MI | Mutual information |
RFE | Recursive Feature Elimination |
HWEE | Hot Water Energy Efficiency |
HWEVE | Hot Water Environment Efficiency |
HVAC | Heating, Ventilation, Air Conditioning |
WIEE | Windows Energy Efficiency |
WENE | Windows Environment Efficiency |
WEE | Walls Energy Efficiency |
WENE | Walls Environment Efficiency |
REE | Roof Energy Efficiency |
RENE | Roof Environment Efficiency |
MHEE | Main Heat Energy Efficiency |
MHENE | Main Heat Environment Efficiency |
MHCEE | Main Heat Control Energy Efficiency |
MHCENE | Main Heat Control Environment Efficiency |
LEE | Lighting Energy Efficiency |
LENE | Lighting Environment Efficiency |
CEE | Current Energy Efficiency |
EIC | Environment Impact Current |
ECC | Energy Consumption Current |
CEC | CO2 Emissions Current |
CEPFA | CO2 Emissions Current per Floor Area |
LCC | Lighting Cost Current |
HCC | Heating Cost Current |
HWCC | Hot Water Cost Current |
TFA | Total Floor Area |
EO | Extension Count |
NAR | Number of Habitable Rooms |
NHR | Number of Heated Rooms |
LEL | Low-Energy Lighting |
SAP | Standard Assessment Procedure |
RdSAP | reduced Standard Assessment Procedure |
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Cite | Techniques Used | Context | Key Findings |
---|---|---|---|
[33] | LR | Building features analysis | Assessed the impact of various features on energy performance. |
[34] | KNN | Residential energy consumption | Predicted historical energy usage and identified characteristic patterns. |
[36] | KNN | Pre- and post-retrofit energy data | Demonstrated suitability to predicting energy-saving potential. |
[37] | SVR | Meteorological, material, and occupancy data | Modeled energy performance under heterogeneous conditions. |
[38] | DT | Historical energy and building characteristics | Effectively identified critical energy-saving areas. |
[39] | RF | Building size, insulation, and occupancy patterns | Captured complex relationships for accurate energy use prediction. |
[40] | RF | Historical consumption and building data | Validated RF’s performance in predicting energy demand. |
[41] | GB | Commercial building energy consumption | Achieved superior forecasting accuracy compared to other approaches. |
[44] | ANN, SVM, Gaussian-based regressions, clustering | Building energy performance forecasting | Provided a comprehensive analysis of primary ML techniques. |
[12] | XGBoost, ANN, Degree-Day OLS Regression | Energy model construction | Demonstrated enhanced accuracy and efficiency in energy prediction. |
[45] | Taxonomy of ML Techniques | Energy consumption prediction | Offered a comparative examination of various ML algorithms. |
[46] | SGD, NLP | Classification and Prediction by analysing text data | Conducted automatic risk assessment on text data and effectively predicted it. |
Category | Feature | Description |
---|---|---|
Building | Total Floor Area | The total floor area measured in square meters. |
Number of Extensions | The number of extensions present in the property. | |
Number of Habitable Rooms | The total number of habitable rooms within the property. | |
Number of Heated Rooms | The total number of rooms in the property that are heated. | |
Low-Energy Lighting | The percentage of lighting within the property that utilizes low-energy solutions. | |
Window Energy Efficiency | The energy efficiency rating of the windows. | |
Wall Energy Efficiency | The energy efficiency rating of the walls. | |
Energy | Current Energy Efficiency Rating | The current energy efficiency rating of the property. |
Current Energy Consumption | The total energy consumption of the property in its current state. | |
Main Heating System Efficiency | The energy efficiency rating of the primary heating system. | |
Main Heating Control Efficiency | The energy efficiency rating of the primary heating control system. | |
Lighting Efficiency | The energy efficiency rating of the lighting system. | |
Hot Water System Efficiency | The energy efficiency rating of the hot water system. | |
Roof Energy Efficiency | The energy efficiency rating of the roof. | |
Window Energy Efficiency | The energy efficiency rating of the windows. |
Category | Feature | Description |
---|---|---|
Environmental | Current Environmental Impact Rating | The environmental impact rating of the property in its current state. |
Current CO2 Emissions | The total amount of CO2 emissions produced by the property. | |
CO2 Emissions per Floor Area | The amount of CO2 emissions produced per square meter of floor area. | |
Main Heating System Environmental Efficiency | The environmental efficiency rating of the primary heating system. | |
Main Heating Control Environmental Efficiency | The environmental efficiency rating of the primary heating control system. | |
Lighting Environmental Efficiency | The environmental efficiency rating of the lighting system. | |
Hot Water System Environmental Efficiency | The environmental efficiency rating of the hot water system. | |
Roof Environmental Efficiency | The environmental efficiency rating of the roof. | |
Window Environmental Efficiency | The environmental efficiency rating of the windows. | |
Cost | Annual Lighting Cost | The estimated annual cost incurred for lighting. |
Annual Heating Cost | The estimated annual cost incurred for heating. | |
Annual Hot Water Cost | The estimated annual cost incurred for hot water usage. | |
Current Energy Consumption | The total energy consumption of the property in its current state. |
Models | Selected Hyperparameters | Best Hyperparameters |
---|---|---|
Linear Regression | {} | {} |
KNN | {n_neighbors: [3, 5, 7]} | {n_neighbors: 5} |
SVR | {C: [0.1, 1, 10], gamma: [0.1, 1, ’cale’, ’auto’]} | {C: 10, gamma: ’cale’} |
Decision Tree | {max_depth: [None, 10, 20], min_samples_split: [2, 5, 10]} | {max_depth: 10, min_samples_split: 10} |
Random Forest | {n_estimators: [50, 100, 200], max_depth: [None, 10, 20], min_samples_split: [2, 5, 10]} | {max_depth: 20, min_samples_split: 2, n_estimators: 100} |
Extra Trees | {n_estimators: [50, 100, 200], max_depth: [None, 10, 20], min_samples_split: [2, 5, 10]} | {max_depth: 20, min_samples_split: 2, n_estimators: 100} |
Gradient Boosting | {n_estimators: [50, 100, 200], learning_rate: [0.01, 0.1, 0.5], max_depth: [3, 5, 10]} | {learning_rate: 0.1, max_depth: 5, n_estimators: 200} |
XGBoost | {n_estimators: [50, 100, 200], learning_rate: [0.01, 0.1, 0.5], max_depth: [3, 5, 10]} | {learning_rate: 0.1, max_depth: 5, n_estimators: 200} |
LightGBM | {n_estimators: [50, 100, 200], learning_rate: [0.01, 0.1, 0.5], max_depth: [3, 5, 10]} | {learning_rate: 0.1, max_depth: 5, n_estimators: 200} |
Ranking | Correlation | Linear Regression | Random Forest | Gradient Boosting | PC1 | Mutual Information | RFE | LASSO |
---|---|---|---|---|---|---|---|---|
1 | CEPFA | ECC | CEPFA | CEPFA | LEE | CEPFA | CEPFA | ECC |
2 | ECC | CEC | CEC | CEC | LENE | ECC | ECC | CEC |
3 | CEC | CEPFA | HCC | HCC | LEL | CEC | CEC | HWEE |
4 | HCC | HCC | ECC | ECC | RENE | HCC | TFA | WEE |
5 | HWCC | HWCC | MHEE | MHEE | REE | WENE.1 | HCC | MHEE |
6 | EO | NAR | HWCC | HWCC | MHCEE | HWEE | MHEE | REE |
7 | LCC | MHCENE | MHENE | MHENE | MHCENE | WEE | MHENE | LEL |
8 | TFA | MHCEE | LCC | LCC | WEE | HWEVE | NAR | WIEE |
9 | NAR | LEE | TFA | TFA | WENE.1 | HWCC | NHR | MHCEE |
10 | NHR | LENE | HWEE | HWEE | WENE | MHEE | HWEE | WENE.1 |
11 | LENE | EO | LEL | LEL | WIEE | RENE | LCC | RENE |
12 | LEE | LEL | HWEVE | HWEVE | LCC | REE | LEL | LEE |
13 | LEL | NHR | WENE.1 | WENE.1 | HWEE | MHCENE | HWCC | MHCENE |
14 | WENE | LEE | WEE | WEE | HWEVE | MHCEE | WEE | MHENE |
15 | WIEE | MHENE | NHR | NHR | ECC | WIEE | HWEVE | HWEVE |
16 | REE | EO | NAR | NAR | CEC | MHENE | MHCEE | WENE |
17 | RENE | WIEE | EO | EO | CEPFA | WENE | WENE.1 | NHR |
18 | MHCEE | WENE | RENE | RENE | MHEE | LEL | RENE | NAR |
19 | MHCENE | REE | REE | REE | MHENE | TFA | MHCENE | EO |
20 | MHENE | REE | WENE | WENE | HCC | NHR | REE | TFA |
21 | WEE | RENE | WIEE | WIEE | NAR | LENE | WENE | HWCC |
22 | WENE.1 | WEE | MHCEE | MHCEE | HWCC | LEE | WIEE | HCC |
23 | MHEE | MHENE | MHCENE | MHCENE | TFA | LCC | EO | LCC |
24 | HWEVE | WENE.1 | LENE | LENE | NHR | EO | LENE | CEPFA |
25 | HWEE | MHEE | LEE | LEE | EO | NAR | LEE | LENE |
Models | MSE | MAE | R2 | RMSE | MAPE | Adjusted R2 |
Linear Regression | 12.590 | 2.091 | 0.873 | 3.548 | 5.444 | 0.873 |
KNN | 11.167 | 1.993 | 0.887 | 3.342 | 4.731 | 0.887 |
SVR | 7.244 | 1.455 | 0.927 | 2.692 | 3.595 | 0.927 |
Decision Tree | 9.483 | 1.690 | 0.903 | 3.079 | 4.045 | 0.903 |
Random Forest | 5.455 | 1.150 | 0.945 | 2.336 | 3.004 | 0.945 |
Extra Trees | 5.131 | 1.162 | 0.948 | 2.265 | 2.943 | 0.948 |
Gradient Boosting | 4.698 | 1.261 | 0.953 | 2.167 | 2.744 | 0.953 |
XGBoost | 4.703 | 1.256 | 0.953 | 2.169 | 2.773 | 0.953 |
LightGBM | 4.713 | 1.267 | 0.952 | 2.171 | 2.794 | 0.952 |
(a) Using 3-Fold Data | ||||||
Models | MSE | MAE | R2 | RMSE | MAPE | Adjusted R2 |
Linear Regression | 12.574 | 2.091 | 0.873 | 3.546 | 5.421 | 0.873 |
KNN | 10.704 | 1.942 | 0.892 | 3.272 | 4.608 | 0.892 |
SVR | 7.057 | 1.442 | 0.929 | 2.656 | 3.411 | 0.929 |
Decision Tree | 9.200 | 1.675 | 0.907 | 3.033 | 3.865 | 0.907 |
Random Forest | 5.308 | 1.128 | 0.946 | 2.304 | 2.870 | 0.946 |
Extra Trees | 5.014 | 1.144 | 0.949 | 2.239 | 2.820 | 0.949 |
Gradient Boosting | 4.609 | 1.245 | 0.953 | 2.147 | 2.652 | 0.953 |
XGBoost | 4.555 | 1.243 | 0.954 | 2.134 | 2.687 | 0.954 |
LightGBM | 4.590 | 1.253 | 0.954 | 2.142 | 2.720 | 0.954 |
(b) Using 5-Fold Data | ||||||
Models | MSE | MAE | R2 | RMSE | MAPE | Adjusted R2 |
Linear Regression | 12.554 | 2.092 | 0.873 | 3.543 | 5.406 | 0.873 |
KNN | 10.483 | 1.919 | 0.894 | 3.238 | 4.559 | 0.894 |
SVR | 6.977 | 1.437 | 0.929 | 2.641 | 3.368 | 0.929 |
Decision Tree | 9.129 | 1.673 | 0.909 | 3.022 | 3.846 | 0.909 |
Random Forest | 5.159 | 1.115 | 0.948 | 2.271 | 2.850 | 0.948 |
Extra Trees | 4.948 | 1.138 | 0.950 | 2.224 | 2.797 | 0.950 |
Gradient Boosting | 4.521 | 1.242 | 0.954 | 2.126 | 2.636 | 0.954 |
XGBoost | 4.543 | 1.242 | 0.954 | 2.132 | 2.692 | 0.954 |
LightGBM | 4.538 | 1.246 | 0.954 | 2.130 | 2.682 | 0.954 |
(c) Using 7-Fold Data | ||||||
Models | MSE | MAE | R2 | RMSE | MAPE | Adjusted R2 |
Linear Regression | 12.558 | 2.092 | 0.873 | 3.544 | 5.400 | 0.873 |
KNN | 10.336 | 1.904 | 0.895 | 3.215 | 4.492 | 0.895 |
SVR | 6.943 | 1.435 | 0.930 | 2.635 | 3.353 | 0.930 |
Decision Tree | 8.808 | 1.656 | 0.911 | 2.968 | 3.724 | 0.911 |
Random Forest | 5.139 | 1.111 | 0.948 | 2.267 | 2.841 | 0.948 |
Extra Trees | 4.885 | 1.132 | 0.951 | 2.210 | 2.775 | 0.951 |
Gradient Boosting | 4.547 | 1.241 | 0.954 | 2.132 | 2.674 | 0.954 |
XGBoost | 4.526 | 1.236 | 0.954 | 2.127 | 2.681 | 0.954 |
LightGBM | 4.490 | 1.238 | 0.955 | 2.119 | 2.698 | 0.955 |
(d) Using 9-Fold Data | ||||||
Models | MSE | MAE | R2 | RMSE | MAPE | Adjusted R2 |
Linear Regression | 14.334 | 2.115 | 0.862 | 3.786 | 6.316 | 0.862 |
KNN | 11.634 | 1.986 | 0.888 | 3.411 | 4.979 | 0.888 |
SVR | 10.834 | 1.685 | 0.896 | 3.291 | 5.580 | 0.896 |
Decision Tree | 11.391 | 1.568 | 0.890 | 3.460 | 4.071 | 0.884 |
Random Forest | 6.305 | 1.165 | 0.939 | 2.499 | 3.135 | 0.940 |
Extra Trees | 6.023 | 1.151 | 0.942 | 2.454 | 3.125 | 0.942 |
Gradient Boosting | 7.733 | 1.583 | 0.925 | 2.361 | 3.197 | 0.925 |
XGBoost | 5.477 | 1.242 | 0.947 | 2.340 | 2.970 | 0.947 |
LightGBM | 5.575 | 1.331 | 0.946 | 2.361 | 3.197 | 0.946 |
(e) Using whole data |
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Shakeel, H.M.; Iram, S.; Hill, R.; Athar Farid, H.M.; Sheikh-Akbari, A.; Saleem, F. A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings. Buildings 2025, 15, 1275. https://doi.org/10.3390/buildings15081275
Shakeel HM, Iram S, Hill R, Athar Farid HM, Sheikh-Akbari A, Saleem F. A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings. Buildings. 2025; 15(8):1275. https://doi.org/10.3390/buildings15081275
Chicago/Turabian StyleShakeel, Hafiz Muhammad, Shamaila Iram, Richard Hill, Hafiz Muhammad Athar Farid, Akbar Sheikh-Akbari, and Farrukh Saleem. 2025. "A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings" Buildings 15, no. 8: 1275. https://doi.org/10.3390/buildings15081275
APA StyleShakeel, H. M., Iram, S., Hill, R., Athar Farid, H. M., Sheikh-Akbari, A., & Saleem, F. (2025). A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings. Buildings, 15(8), 1275. https://doi.org/10.3390/buildings15081275