SmartBuild RecSys: A Recommendation System Based on the Smart Readiness Indicator for Energy Efficiency in Buildings
Abstract
:1. Introduction
- The first use case concentrates on the thermal envelope of the building and relies on BIM data. This includes essential information, such as the U-value of walls, roofs, and windows, as well as recommendations for insulation materials. By leveraging these data, the RS can provide valuable insights and recommendations for improving the thermal performance of buildings. These recommendations can guide designers and building managers in making informed decisions and implementing energy-efficient design strategies.
- The second use case centers around the SRI data source, which provides information about a building’s readiness for smart services, including building automation systems, lighting control, heating, cooling, ventilation, and more. By analyzing these data, the RS can generate tailored recommendations for enhancing energy efficiency through the integration and implementation of smart technologies. It is important to note that while the recommendations do not guarantee an immediate increase in efficiency, they represent significant opportunities for improvement by adopting the suggested smart-ready services.
2. Background
2.1. Recommendation Systems
2.2. Smart Readiness Indicator
2.3. Building Information Modeling
3. Methodology for Recommendation Systems
3.1. Thermal Envelope
3.1.1. Data Collection and Pre-Processing
- Extracting thermal envelope characteristics from the IFC BIM file of the smart home. For this purpose, a separate code was utilized using the ‘IFCOPENSHELL’ library in Python to extract the relevant thermal envelope characteristics. For extracting the thermal transmittance values of the walls and roof, ‘IFCWALL’ and ‘IFCSLAB’ were used, respectively. To obtain detailed information about the insulation layers and respective thicknesses, ‘IFCMATERIALLAYERSETUSAGE’ was used.
- Collecting data from the Passive House database and formatting of data. For this purpose, data were collected from the official database website. Data were collected manually and then manually formatted in a CSV file.
3.1.2. Machine Learning Algorithms
3.2. HVAC Systems
3.2.1. Data Collection and Pre-Processing
3.2.2. Machine Learning Algorithms
- 1.
- Once the lowest percentage is identified, the domain and the impact are noted (Lighting and energy efficiency in case of Table 3).
- 2.
- Next, for the domain ‘lighting’, the separate CSV file named lighting (extracted from the SRI Excel file in the data collection phase) is used to filter out all instances where ‘energy efficiency’ has an impact value of above zero. All such instances are then traced back to the service name under which they happen. For example, in the lighting domain, there are only two services (Occupancy control of indoor lighting, L-1a, and Control artificial lighting based on outdoor daylight levels, L-2) and in both these services, for at least one functionality level, the impact of energy efficiency is above zero.
- 3.
- Since the RS must be capable of accepting any kind of impact-domain matrix, the code developed for performing the steps described above must be dynamic. This requires, for the identified domain, making a separate code file that contains a function that performs the filtering of smart-ready services based on non-zero values under the identified impact area.
- 4.
- Once the separate code files containing the smart service filter function are in place, they can be called based on the lowest percentage in the impact domain matrix. The output will be identified smart services, which will then be fed to the ML algorithms.
- 5.
- After acquiring the smart-ready services, along with the building type and the construction year, the KNN algorithm using scikit-learn’s K Neighbors Regressor with a single neighbor as parameter (n_neighbors = 1) and the DT algorithm (with the parameters criterion = ‘mse’, splitter = ‘best’, min_samples_split = 2, min_samples_leaf = 1, min_impurity_decrease = 0.0, min_impurity_split = None and presort = ‘deprecated’) was employed to suggest the functionality level for the smart-ready service identified earlier. This is completed based on the similarity of the building information (type and construction year) entered by the user compared to those present in the data used to train the algorithms.
4. Results and Evaluation
4.1. Recommendation System for Thermal Envelope
4.1.1. Numerical Evaluation
4.1.2. Categorical Evaluation
- Accuracy for Wall insulation 60%.
- Accuracy for roof insulation 57%.
4.2. Recommendation System for HVAC Systems
5. Discussion
- First use case
- –
- The results of the numerical variable in the first use case are deemed acceptable. The predicted U value for roofs was 0.1. The thickness value for wall insulation was found to be 0.25. The predicted value for roof insulation was 0.3. Lastly, the U value for windows was measured to be 0.55. These values are within the acceptable ranges mentioned in Table 4. However, the results for the categorical variables are inferior, since the obtained accuracy values range between 50% and 60%. This can be attributed to the limited quantity of building data that were taken into account. To train the ML algorithms, data were taken from the Passive House database in addition to the IFC BIM file of CERTH’s smart home. Since the data were collected manually from the database, data of only 30 buildings were considered in the database. It is also noted that when data were generated artificially, accuracy decreased even further. The utilization of larger datasets has the potential to result in increased accuracy when dealing with categorical variables.
- –
- In addition to conducting a quantitative evaluation of the outcomes of the proposed RS, a qualitative evaluation was also implemented to assess the coherence and validity of the results. For that purpose, different sets of input variables were tested to observe the thickness of insulation and the corresponding U values of the wall and roof. It was observed that when there is an increase in the thickness of the wall and roof insulation, the U values tend to drop. This is in line with the concepts of thermal resistance. An increase in insulation thickness would increase thermal resistance and therefore decrease the U values because the two are inversely proportional. Thus, it was determined that the results obtained from the proposed RS make sense from a domain-knowledge point of view.
- Second use case
- –
- The challenging part about developing an algorithm for an RS using SRI data was the difficulty in finding complete data. As mentioned in previous sections, the SRI data for training the ML algorithm were taken from the available published literature. However, this was only data for 11 buildings, and the results as such would not be very good for a wide range of buildings when using such a small dataset. As a result, out of every three input instances in the RS, two values were predicted correctly.
- –
- From a qualitative perspective, there is a favorable relationship between the year of construction and the SRI score. This means that for a given smart-ready service, the functionality level should be higher if the construction year input by the user is more recent. If the building is old, then it should be lower. This test was performed, and the RS produced the expected results, suggesting a higher functionality level for the same smart-ready service if the building is new; but if it is old, lower functionality levels are suggested.
5.1. Challenges
- The utilization of datasets for training ML algorithms is characterized by a notable limitation in terms of size. In contrast, RSs usually rely on larger datasets, typically consisting of no less than one thousand data points. As a result, the scalability of the proposed RS has not been adequately tested.
- The utilization of the Passive House database was necessary due to the limited availability of BIM data for buildings. Hence, the accessibility of BIM data has the potential to enhance automation and reduce the reliance on manual efforts in the data preparation process.
- The dataset was manually prepared for the first use case due to the absence of a feature in the Passive House database that allows for data retrieval in a tabular format. From a practical point of view, this could potentially present a challenge for developers who intend to create this kind of RS.
- The Passive House database encompasses a comprehensive range of climatic conditions across Europe. Yet, it is less widely adopted outside Europe, resulting in limited availability of climate-specific construction details for regions beyond Europe. This database constraint directly influences the proposed RS.
- Using the Passive House database for training ML algorithms yields recommendations for constructing energy-efficient building envelopes. Moreover, it is important to note that passive house constructions tend to incur higher costs, which may pose a potential challenge despite their high energy efficiency.
- The current SRI framework is characterized by subjectivity, which consequently impacts the recommendations provided by the proposed or any RS. Nevertheless, it is imperative to acknowledge that this issue cannot be ignored unless certain modifications are made to the official SRI framework.
5.2. Future Work
- The algorithmic results for the RSs in both use cases can be enhanced with the incorporation of a cost feature. In the first use case (thermal envelope RS), it is possible to incorporate the cost of insulating materials as a factor. This would enable users to specify their budgetary constraints, hence allowing for personalized suggestions that align with their financial preferences. Similarly, the second use case (HVAC Systems RS) involves including the expenses associated with each building service. This enables the user to make informed decisions on their desired level of automation for a certain building service based on their allocated budget.
- For the first use case, a safety element can be added. Insulation materials are not just selected for the lower U value but also for their resistance to fire in certain climates. This is not considered in this work. Adding this feature would make the RS more practical.
- For the first use case, the EU building stock observatory [40] can also be used as a source of data, as it contains most of the variables included in the dataset prepared for this work, and it does not require any manual form of data entry, as the data are available in CSV files.
- For the first use case, external factors like local regulations with regard to the insulation can also be added to the existing RS.
- The SRI data are quite difficult to collect. Therefore, a central database at the national level would be very helpful for future researchers in the SRI domain.
- With larger datasets for both SRI and BIM, the scalability of the RS can be better tested.
- The subjectivity of the SRI framework needs to be addressed by adding quantitative elements to it.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
BIM | Building Information Modeling |
BREEAM | Building Research Establishment Environmental Assessment Method |
CBECS | Commercial Buildings Energy Consumption Survey |
CERTH | Center for Research and Technology Hellas |
CED | Cumulative Energy Demand |
DL | Deep Learning |
DT | Decision Tree |
EPBD | Energy Performance of Buildings Directive |
EPC | Energy Performance Certificate |
EU | European Union |
GPP | Green Public Procurement |
HVAC | Heating, Ventilation and Air Conditioning |
ICT | Information and Communication Technology |
IFC | Industry Foundation Classes |
KNN | K-Nearest Neighbor |
KPI | Key Performance Indicator |
LCA | Life Cycle Analysis |
MAE | Mean Absolute Error |
ML | Machine Learning |
MSE | Mean Squared Error |
nZEB | nearly Zero Energy Building |
RMSE | Root Mean Square Error |
RS | Recommendation System |
SRI | Smart Readiness Indicator |
TQA | Total Quality Assessment |
U value | Thermal Transmittance |
UNEP-SBCI | United Nations Environment Program’s Sustainable Buildings and Climate Initiative |
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Domain | Impact Areas |
---|---|
Heating | Energy savings |
Cooling | Maintenance and fault prediction |
Domestic hot water | Comfort |
Ventilation | Convenience |
Lighting | Information to occupant |
Dynamic building envelope | Health and well-being |
Electricity generation | Energy demand flexibility |
Electric vehicles charging | |
Monitoring and controlling |
Building Type | Construction Year | Heat Emission Control (H-1a) | Emission Control for TABS (Heating Mode)(H-1b) | ⋯ |
---|---|---|---|---|
Single-family house | 2018 | 3 | NA | ⋯ |
Educational building | 2017 | 2 | 3 | ⋯ |
Educational building | 2015 | 3 | NA | ⋯ |
Educational building | 1988 | 2 | NA | ⋯ |
Educational building | 1994 | 2 | NA | ⋯ |
Educational building | 2000 | 2 | 3 | ⋯ |
Educational building | 2000 | 2 | NA | ⋯ |
Educational building | 2004 | 3 | NA | ⋯ |
Educational building | 2017 | 3 | 3 | ⋯ |
Educational building | 1973 | 2 | NA | ⋯ |
Educational building | 1994 | 3 | NA | ⋯ |
Technical Domains | Energy Efficiency | Energy Flexibility | Comfort | Convenience | Health and Well-Being | Maintenance | Information to Occupants |
---|---|---|---|---|---|---|---|
Heating | 80 | 17 | 75 | 63 | 67 | 50 | 67 |
Domestic hot water | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Cooling | 85 | 17 | 25 | 63 | 67 | 50 | 67 |
Ventilation | 0 | 0 | 26 | 0 | 43 | 50 | 67 |
Lighting | 1 | 0 | 20 | 13 | 0 | 0 | 0 |
Dynamic building envelope | 20 | 0 | 20 | 17 | 0 | 0 | 0 |
Electricity | 80 | 56 | 0 | 25 | 0 | 0 | 67 |
Electric vehicle charging | 0 | 25 | 0 | 25 | 0 | 0 | 67 |
Monitoring and control | 50 | 67 | 67 | 59 | 50 | 64 | 78 |
Building Envelope Elements | Acceptable Range |
---|---|
Wall U value | 0.1–0.25 |
Roof U value | 0.07–0.2 |
Wall insulation thickness | 0.2–0.3 |
Roof insulation thickness | 0.025–0.035 |
Window U value | 0.5–0.6 |
Building Envelope Component | MAE | RMSE |
---|---|---|
Wall U value | 0.0326 | 0.0423 |
Roof U value | 0.0349 | 0.0430 |
Wall insulation thickness | 0.0263 | 0.0329 |
Roof insulation thickness | 0.0238 | 0.0316 |
Window U value | 0.0471 | 0.0647 |
Algorithm | Wall U Value | Roof U Value | Wall Insulation Thickness | Roof Insulation Thickness | Window U Value |
---|---|---|---|---|---|
Random Forest | MAE = 0.0326 | MAE = 0.0349 | MAE = 0.0263 | MAE = 0.0238 | MAE = 0.0471 |
RMSE = 0.0423 | RMSE = 0.0430 | RMSE = 0.0329 | RMSE = 0.0316 | RMSE = 0.0647 | |
Linear Regression | MAE = 0.0370 | MAE = 0.0331 | MAE = 0.0251 | MAE = 0.0305 | MAE = 0.0459 |
RMSE = 0.0449 | RMSE = 0.0387 | RMSE = 0.0300 | RMSE = 0.0455 | RMSE = 0.0554 | |
K-Nearest Neighbor | MAE = 0.0374 | MAE = 0.0349 | MAE= 0.0234 | MAE = 0.0291 | MAE = 0.0512 |
RMSE = 0.0464 | RMSE = 0.0436 | RMSE = 0.0294 | RMSE = 0.0393 | RMSE = 0.0723 |
Algorithm | Wall Insulation | Roof Insulation |
---|---|---|
Linear Regression | 30.3 | 40.4 |
Logistic Regression | 16.67 | 13.3 |
Decision Tree Classifier | 60 | 57 |
K-Nearest Neighbor | 11 | 0 |
Actual Functionality Level | Predicted Functionality Level |
---|---|
2 | 2 |
3 | 2 |
2 | 2 |
Actual Functionality Level | Predicted Functionality Level |
---|---|
1 | 0 |
0 | 1.5 |
1 | 1 |
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Siddique, M.T.; Koukaras, P.; Ioannidis, D.; Tjortjis, C. SmartBuild RecSys: A Recommendation System Based on the Smart Readiness Indicator for Energy Efficiency in Buildings. Algorithms 2023, 16, 482. https://doi.org/10.3390/a16100482
Siddique MT, Koukaras P, Ioannidis D, Tjortjis C. SmartBuild RecSys: A Recommendation System Based on the Smart Readiness Indicator for Energy Efficiency in Buildings. Algorithms. 2023; 16(10):482. https://doi.org/10.3390/a16100482
Chicago/Turabian StyleSiddique, Muhammad Talha, Paraskevas Koukaras, Dimosthenis Ioannidis, and Christos Tjortjis. 2023. "SmartBuild RecSys: A Recommendation System Based on the Smart Readiness Indicator for Energy Efficiency in Buildings" Algorithms 16, no. 10: 482. https://doi.org/10.3390/a16100482