Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques
Abstract
1. Introduction
- To build and fine-tune machine learning models for assessing the level of smart building integration into a smart city and enabling predictive and scalable evaluation;
- To evaluate, from an evidence-based perspective, the impact of smart building integration on smart city efficiency, resilience, and environmental sustainability, thereby operationalising integration at the core of urban performance;
- To inform urban planners and decision-makers on how smart buildings’ integration in smart cities could be enhanced to help policymaking and strategic thinking, by utilising a validated and explainable AI-based evaluation framework.
2. Literature Review
2.1. Assessment Frameworks for Smart Buildings and Smart Cities
2.2. Application of Machine Learning Techniques in Smart Building and Smart City Assessment
3. Methodology
3.1. An Overview of the Theoretical Framework
3.2. Development of the ML Model for Smart Building Integration into a Smart City
- Step 1: Data collection, examination, and pre-processing;
- Step 2: ML model development (training, testing, optimisation);
- Step 3: ML model interpretation by using the permutation importance technique;
- Step 4: ML model application for future predictions.
- Step 1: Data collection, examination, and pre-processing
- Handling missing data: Missing values were explored after the collection of data and were addressed as a crucial aspect for the validity of any machine learning application. Two principles were used in this work to handle missing data: (1) the technical logic of the smart building systems’ interrelation and interdependency, and (2) data integrity and distribution consistency. Despite the fact that smart building features are often interconnected (e.g., if energy storage systems are installed, then energy monitoring infrastructure is deployed), the missing entries were contextually evaluated instead of treated as randomly dropped entries. Logic-based imputation was applied where relevant missing values were inferred based on system-level dependencies and prior distributions. In cases of features that did not have reliable inferential patterns or the values were missing completely at random, those records were removed or indicated to ensure structural bias did not exist. This approach guarantees the internal consistency of the output dataset and its suitability for prediction modelling, in particular when complex interactions among building properties have an effect on the integration results.
- Feature engineering: Total points were calculated for each building based on the theoretical framework. The establishment range of total points was observed in the dataset by determining the minimum and maximum of the feature aggregation, and the range of total points was divided into five equal intervals, representing five distinct class levels. Buildings were then categorised into specific class levels based on their total points. Furthermore, scores for total integration and the impact on the efficiency, resilience, and environmental sustainability of smart city performance have been aggregated based on the factors’ actual impact presented in Appendix A. These scores were further assigned, accordingly, to the potential class level [80] of the SC performance aspects for each building.
- Statistical normality tests: Understanding dataset distribution remains relevant for predictive modelling. Although normality tests become negligible for samples over 100 [81], the Shapiro–Wilk test was conducted to assess whether the dataset followed a normal distribution. This method is particularly useful with small sample sizes of datasets by testing the null hypothesis that the data are normally distributed. Rejection of the null hypothesis suggests non-normally distributed data.
- Data normalisation or standardisation is a standard procedure in machine learning and statistical analysis that ensures that no single variable disproportionately influences the model due to its scale, especially when models are sensitive to feature magnitude, such as KNN and SVR [82]. The two most widely used normalisation methods are min–max scaling (rescale the values to a [0, 1] range) and z-score standardisation (centre the data by subtracting the mean and scaling with the standard deviation to have unit variance).In this study, the features were all binary representations (0 or 1) of the availability of a smart building service in a building; thus, no transformation was necessary, as the features already had the same scale and similar semantic meanings (Appendix B and Appendix C). Attempts to apply normalisation might blur the discrete data. Therefore, the dataset was directly used in its raw binary format for model training and testing.
- Data balancing: Given the potential for class imbalance in the target variable (integration level), the SMOTE (Synthetic Minority Over-Sampling) technique was applied to oversample the minority class and enhance model performance on imbalanced data [78]. Class imbalance occurs when one class has significantly more instances than another. This can lead to biassed models that favour the majority class. SMOTE addresses this issue by generating synthetic samples of the minority class to balance the class distribution, improving model performance for the underrepresented class.
- Data splitting: Before training the algorithms, the dataset was split into training (70%) and testing (30%) sets, ensuring reproducibility and unbiased model evaluation.
- Step 2: ML model development
- Step 3: ML model interpretation
- Step 4: ML model application for future predictions
4. Results
4.1. Data Collection, Examination, and Pre-Processing
4.2. ML Model Development
4.2.1. Training, Testing, and Optimisation
4.2.2. Selection of the ML Model
4.3. Model Interpretation
4.4. Case Study
4.4.1. Smart Building Integration into Smart City Predictions
4.4.2. Insights into Smart Building Integration into Smart City Enhancement
4.5. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Evaluation Framework for Smart Building Integration into Smart City [31]
Smart City Infrastructure Domain | Smart Building Services | Impact on the Smart City Performance | Smart City Infrastructure Domain Importance | Factor Score | Smart City Infrastructure Domain Impact, % | |||
Efficiency | Resilience | Environmental Sustainability | ||||||
Energy | E1 | Electrical Energy Storage (Battery) | 2 | 2 | 1 | 5 | 25 | |
E2 | Shared Electrical Energy Storage | 2 | 2 | 1 | 25 | |||
E3 | Ability to Work Off-Grid (Renewable Energy Sources: Solar and Wind) | 1 | 2 | 1 | 20 | |||
E7 | Energy Usage Monitoring and Control and Demand Side Management | 2 | 1 | 2 | 25 | |||
E5 | Smart Heating, Cooling, and Hot Water Preparation | 2 | 2 | 2 | 30 | |||
E6 | Thermal Energy Storage | 2 | 2 | 1 | 25 | |||
E7 | Shared Thermal Energy Storage | 2 | 2 | 2 | 30 | |||
180 | 32.67% | |||||||
Mobility | M1 | Smart EV Charging | 2 | 1 | 2 | 4 | 20 | |
M2 | Carpooling–Ride Sharing | 2 | 1 | 2 | 20 | |||
M3 | Smart Parking Management System (e-Parking) | 2 | 1 | 1 | 16 | |||
M4 | Sharing Parking Space | 2 | 0 | 1 | 12 | |||
M5 | Online Video Surveillance | 1 | 2 | 1 | 16 | |||
M6 | Last Mile Driving | 2 | 0 | 1 | 12 | |||
96 | 17.42% | |||||||
Water | W1 | Smart Water Mixtures | 2 | 1 | 2 | 4 | 20 | |
W2 | Smart Water Monitoring and Shut-Off (Leak Detection and Prevention) | 2 | 2 | 2 | 24 | |||
W3 | Smart Water Irrigation System | 2 | 1 | 2 | 20 | |||
W4 | Smart Water Meter | 2 | 1 | 2 | 20 | |||
W5 | Greywater Recycling | 2 | 2 | 2 | 24 | |||
W6 | Rainwater Collection (Harvesting) and Reuse | 2 | 2 | 2 | 24 | |||
132 | 23.96% | |||||||
Waste Management | WS1 | Smart Waste Containers (Smart Bins) | 2 | 1 | 2 | 3 | 15 | |
WS2 | Automation and Robotic Waste Collection (Underground Waste Collection) | 2 | 2 | 2 | 18 | |||
33 | 5.99% | |||||||
Security | S1 | Smart Monitoring and Data Analytics of the Surrounding Environment (Face Detection and Car Plate Detection) | 1 | 2 | 1 | 5 | 20 | |
S2 | Smart Fire Management | 2 | 2 | 1 | 25 | |||
S3 | Disaster Event Communication Management | 2 | 2 | 1 | 25 | |||
S4 | Smart Security Lights | 1 | 2 | 1 | 20 | |||
S5 | Integrated Sensor Solutions | 1 | 2 | 1 | 20 | |||
110 | 19.96% | |||||||
Ideal Integration Score | 47 | 40 | 39 | 21 | 551 | 100% |
Appendix B. Case Study Buildings and Their Present Services
Smart City Infrastructure Domain | Smart Building Services | Building 1 | Building 2 | Building 3 | Building 4 |
Energy | Electrical Energy Storage (Battery) | 1 | 1 | 1 | 1 |
Shared Electrical Energy Storage | 0 | 0 | 0 | 1 | |
Ability to Work Off-Grid (Renewable Energy Sources: Solar and Wind) | 1 | 1 | 1 | 1 | |
Energy Usage Monitoring and Control and Demand Side Management | 1 | 1 | 1 | 1 | |
Smart Heating, Cooling, and Hot Water Preparation | 1 | 0 | 0 | 1 | |
Thermal Energy Storage | 1 | 1 | 1 | 0 | |
Shared Thermal Energy Storage | 0 | 1 | 0 | 0 | |
Mobility | Smart EV Charging | 0 | 0 | 1 | 1 |
Carpooling–Ride Sharing | 0 | 1 | 1 | 0 | |
Smart Parking Management System (e-Parking) | 0 | 1 | 1 | 1 | |
Sharing Parking Space | 0 | 0 | 1 | 1 | |
Online Video Surveillance | 0 | 1 | 1 | 1 | |
Last Mile Driving | 1 | 0 | 0 | 0 | |
Water | Smart Water Mixtures | 1 | 1 | 1 | 1 |
Smart Water Monitoring and Shut-Off (Leak Detection and Prevention) | 1 | 1 | 1 | 1 | |
Smart Water Irrigation System | 0 | 1 | 0 | 0 | |
Smart Water Meter | 1 | 1 | 1 | 1 | |
Greywater Recycling | 0 | 0 | 1 | 1 | |
Rainwater Collection (Harvesting) and Reuse | 1 | 1 | 1 | 1 | |
Waste Management | Smart Waste Containers (Smart Bins) | 0 | 0 | 1 | 1 |
Automation and Robotic Waste Collection (Underground Waste Collection) | 0 | 0 | 1 | 1 | |
Security | Smart Monitoring and Data Analytics of the Surrounding Environment (Face Detection and Car Plate Detection) | 0 | 1 | 1 | 1 |
Smart Fire Management | 1 | 1 | 1 | 1 | |
Disaster Event Communication Management | 0 | 1 | 0 | 1 | |
Smart Security Lights | 1 | 0 | 1 | 1 | |
Integrated Sensor Solutions | 1 | 1 | 1 | 1 | |
Total number of actual services | 13 | 17 | 20 | 21 |
Appendix C. Case Study Buildings and Their Newly Added Services (Yellow) for the Enhanced Integration
Smart City Infrastructure Domain | Smart Building Services | Building 1 | Building 2 | Building 3 | Building 4 |
Energy | Electrical Energy Storage (Battery) | 1 | 1 | 1 | 1 |
Shared Electrical Energy Storage | 1 | 1 | 1 | 1 | |
Ability to Work Off-Grid (Renewable Energy Sources: Solar and Wind) | 1 | 1 | 1 | 1 | |
Energy Usage Monitoring and Control and Demand Side Management | 1 | 1 | 1 | 1 | |
Smart Heating, Cooling, and Hot Water Preparation | 1 | 1 | 0 | 1 | |
Thermal Energy Storage | 1 | 1 | 1 | 1 | |
Shared Thermal Energy Storage | 1 | 1 | 0 | 0 | |
Mobility | Smart EV Charging | 1 | 0 | 1 | 1 |
Carpooling–Ride Sharing | 0 | 1 | 1 | 0 | |
Smart Parking Management System (e-Parking) | 0 | 1 | 1 | 1 | |
Sharing Parking Space | 0 | 1 | 1 | 1 | |
Online Video Surveillance | 0 | 1 | 1 | 1 | |
Last Mile Driving | 1 | 0 | 0 | 0 | |
Water | Smart Water Mixtures | 1 | 1 | 1 | 1 |
Smart Water Monitoring and Shut-Off (Leak Detection and Prevention) | 1 | 1 | 1 | 1 | |
Smart Water Irrigation System | 0 | 1 | 0 | 1 | |
Smart Water Meter | 1 | 1 | 1 | 1 | |
Greywater Recycling | 1 | 1 | 1 | 1 | |
Rainwater Collection (Harvesting) and Reuse | 1 | 1 | 1 | 1 | |
Waste Management | Smart Waste Containers (Smart Bins) | 0 | 1 | 1 | 1 |
Automation and Robotic Waste Collection (Underground Waste Collection) | 0 | 0 | 1 | 1 | |
Security | Smart Monitoring and Data Analytics of the Surrounding Environment (Face Detection and Car Plate Detection) | 1 | 1 | 1 | 1 |
Smart Fire Management | 1 | 1 | 1 | 1 | |
Disaster Event Communication Management | 1 | 1 | 1 | 1 | |
Smart Security Lights | 1 | 0 | 1 | 1 | |
Integrated Sensor Solutions | 1 | 1 | 1 | 1 | |
Total number of actual services | 19 | 22 | 22 | 23 | |
Number of newly added services | 6 | 5 | 2 | 2 |
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ML Algorithm | Key Performance Characteristics | Rationale of Selection/Limitations | References |
---|---|---|---|
KNN | Simple, interpretable, and effective for small datasets. Performance is highly sensitive to feature scaling and irrelevant attributes. No training time; prediction can be computationally expensive. | Chosen for its simplicity and efficacy in classification challenges predicated on feature similarity. Used as a baseline model to compare against more complex algorithms. Sensitive to scales and lacks interpretability. | [83,84] |
SVR | Strong generalisation ability. Handles high-dimensional feature spaces efficiently. Performs well even with limited data when properly regularised. | Chosen for its robustness, well-suited for complex, high-dimensional data, and effectiveness in achieving clear margins of separation due to its ability to model non-linear relationships using the Kernel function. Requires Kernel tuning. | [84,85] |
RF | High accuracy with low variance. Effective for ranking the features’ importance. Handles missing data and mixed variable types. | Used for its ensemble learning method, which generates several Decision Trees and combines their results. This gives it great accuracy and helps it deal with noise and overfitting. Tends to overfit small data. | [83,84,86] |
AdaBoost | Effective on moderately clean and balanced datasets. Boosting algorithm focusing on correcting predecessor errors. | Chosen for its Adaptive Boosting method, which focusses on reducing mistakes by changing the weights of misclassified instances over and over again. Less effective in noisy datasets. | [83,84,85] |
DT | Fully interpretable, with clear splitting rules. Prone to overfitting, but useful for benchmarking. Fast computing and low complexity. | Easy to understand and follow for the decision making process. Served as a baseline to contrast with ensemble methods (RF and Extra Tree). Prone to overfitting. | [76,83,84] |
ET | Similar to RF but with randomised splits for faster training. Generally, less prone to overfitting on large datasets. Robust for high-dimensional datasets. | A variation in RF increases diversity through greater randomness in feature splitting and data sampling, improving variance reduction. Tested as a variant of RF to assess the impact of randomness on integration predictions. Less robust on heterogeneous datasets. | [83] |
ML Algorithm | Tuned Hyperparameters | Functions of Hyperparameters | Impacts on Performance |
---|---|---|---|
KNN | n_neighbours, weight, metric | n_neighbours: defines locality size. Weights: adjust the distance influence. Metric: chooses a similarity function. | Affects the model’s ability to capture local structures in the data. |
SVR | C, Epsilon, Kernel | C: controls the trade-off between training error and model complexity. Epsilon: defines the margin of tolerance. Kernel: determines the type of non-linearity. | Appropriate parameter tuning may explore the trade-off between bias and variance and improve generalisation, and Kernel choice significantly impacts on effectively capturing non-linear relations and that in turn affects the flexibility and complexity of decision boundary. |
RF | n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features | n_estimators: sets the number of Decision Trees in the ensemble. Typically, more trees reduce variance and improve performance. max_depth: reduces overfitting. min_samples_split: is required to split an internal node. Larger values make the model more conservative. min_samples_leaf: higher values reduce complexity and prevent overfitting. max_features: proportion or number of features considered at each split. Lower values increase randomness, which improves generalisation and reduces overfitting. | Impacts accuracy and resistance to overfitting by limiting depth and adjusting split criteria; more estimators increase stability but may increase computation. |
AdaBoost | n_estimators, learning_rate | n_estimators: sets the number of weak learners. learning_rate: determines the weight of each learner’s contribution. | Has a significant impact on learning stability and enhances focus on misclassified instances. Low learning rates with more estimators improve robustness, while high values risk overfitting or instability. |
DT | max_depth, min_samples_split, min_samples_leaf | max_depth: limits how deep the tree can grow. A shallower tree generalises better; deeper trees may overfit. min_samples_split: the higher values make the tree more conservative and reduce model complexity. min_samples_leaf: refers to the number of samples required to be at a leaf node. Controls the granularity of decision boundaries. | Tuning ensures the balance between capturing the structure and avoiding high-variance errors. |
ET | n_estimators, max_depth, min_samples_split, min_sample_leaf, max_features | n_estimators: refers to the number of trees in the ensemble. More trees generally improve stability and reduce variance. max_depth: controls the depth of each tree. Shallow trees generalise better; deep trees may memorise noise. min_samples_split: controls the minimum samples to split a node. min_samples_leaf: refers to the minimum number of samples required to be at a leaf node. max_features: controls how many features to consider when looking for the best split. Lower values increase randomness. | Greater randomness reduces variance and overfitting; proper depth and minimum split tuning improve generalisation on diverse datasets. |
Class | Min Score | Max Score |
---|---|---|
1 | 288 | 332 |
2 | 333 | 377 |
3 | 378 | 422 |
4 | 423 | 467 |
5 | 468 | 512 |
Class | Efficiency | Resilience | Environmental Sustainability | |||
---|---|---|---|---|---|---|
Min Score | Max Score | Min Score | Max Score | Min Score | Max Score | |
1 | 24 | 31 | 18 | 24 | 19 | 25 |
2 | 32 | 39 | 25 | 31 | 26 | 32 |
3 | 40 | 47 | 32 | 38 | 33 | 39 |
Model | Hyperparameter Setting | Value Range |
---|---|---|
KNN | neighbours = (1, 30) weight = (0, 1) metric = (0, 1) | neighbours = (5.525, 7.158, 10.95, 9.613, 9.258) weight = (0.156, 0.181, 0.968, 0.047, 0.977) metric = (0.598, 0.832, 0.0041, 0.916, 0.885) |
SVR | C = (0.1, 10.0) Epsilon = (0.01, 1.0) Kernel = (0, 1) | C = (6.027, 7.11, 8.261, 5.174, 3.5822) Epsilon = (0.1645, 0.0338, 0.04605, 0.03093, 0.03123) Kernel = (0.156, 0.9699, 0.991, 0.1038, 0.0124) |
RF | n_estimators = (50, 500) max_depth = (3, 50) min_samples_split = (2, 20) min_sample_leaf = (1, 10) max_features = (0.1, 1.0) | n_estimators = (181.1, 255.2, 241.1, 239.7, 247.1) max_depth = (11.62, 31.76, 40.43, 28.37, 36.85) min_samples_split = (9.775, 8.595, 6.335, 2.574, 2.787) min_sample_leaf = (5.723, 3.629, 2.473, 1.777, 1.279) max_features = (0.373, 0.225, 0.714, 0.995, 0.563) |
AdaBoost | n_estimators = (50, 300) learning_rate = (0.01, 1.0) | n_estimators = (199.7, 227, 271) learning_rate = (0.734, 0.605, 0.794) |
DT | max_depth = (3, 50) min_sample_split = (2, 20) min_samples_leaf = (1, 10) | max_depth = (31.14, 49.1) min_sample_split = (4.88, 6.963) min_samples_leaf = (2.404, 1.272) |
ET | n_estimators = (50, 500) max_depth = (3, 50) min_samples_split = (2, 20) min_sample_leaf = (1, 10) max_features = (0.1, 1.0) | n_estimators = (368.6, 181.1, 255.2, 154.6) max_depth = (10.33, 11.62, 31.76, 30.45) min_samples_split = (12.82, 9.775, 8.595, 3.881) min_sample_leaf = (8.796, 5.723, 3.629, 1.322) max_features = (0.1523, 0.3738, 0.2255, 0.143) |
Building No. | City, Country | Year | Type | Area (m2) | Floors No | Rating System |
---|---|---|---|---|---|---|
Building 1 | Houston, USA | 2013 | Commercial; Office | 130,000 | 53 | N/A |
Building 2 | Kuala Lumpur, Malaysia | 2017 | Office | 62,000 | 45 | Green Mark |
Building 3 | Dubai, UAE | 2019 | Commercial Building; Office | 59,000 | 15 | LEED |
Building 4 | Dubai, UAE | 2020 | Commercial; Warehouse; Office | 86,000 | 32 | LEED |
Available Services | Total Integration | Efficiency | Resilience | Environmental Sustainability | |
Class Level | Class Level | Class Level | Class Level | ||
Building 1 | 13/26 | 1 | 1 | 1 | 1 |
Building 2 | 17/26 | 2 | 1 | 2 | 1 |
Building 3 | 20/26 | 3 | 2 | 2 | 2 |
Building 4 | 21/26 | 4 | 2 | 3 | 2 |
Available Services | Total Integration | Efficiency | Resilience | Environmental Sustainability | |
---|---|---|---|---|---|
Class Level | Class Level | Class Level | Class Level | ||
Building 1 | 19/26 | 4 | 2 | 3 | 2 |
Building 2 | 22/26 | 5 | 3 | 3 | 3 |
Building 3 | 22/26 | 4 | 2 | 3 | 2 |
Building 4 | 23/26 | 5 | 3 | 3 | 3 |
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Shahrabani, M.M.N.; Apanaviciene, R. Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques. Buildings 2025, 15, 2031. https://doi.org/10.3390/buildings15122031
Shahrabani MMN, Apanaviciene R. Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques. Buildings. 2025; 15(12):2031. https://doi.org/10.3390/buildings15122031
Chicago/Turabian StyleShahrabani, Mustafa Muthanna Najm, and Rasa Apanaviciene. 2025. "Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques" Buildings 15, no. 12: 2031. https://doi.org/10.3390/buildings15122031
APA StyleShahrabani, M. M. N., & Apanaviciene, R. (2025). Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques. Buildings, 15(12), 2031. https://doi.org/10.3390/buildings15122031