CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors
Abstract
:1. Introduction
2. Background Literature
- Business Understanding and Data Understanding: Identify key physical attributes influencing energy consumption, acknowledging that specific building characteristics significantly affect energy use, and review previous energy prediction models.
- Data Preparation and Modelling: Investigate different machine learning algorithms on Wi-Fi connection data to predict occupancy, aiming to enhance our dataset by improving the accuracy of energy prediction models.
- Modelling and Evaluation: Predict the total energy consumption by integrating diverse data sources, including environmental conditions and building operational data. This comprehensive approach aims to capture the multifaceted nature of energy consumption in buildings.
- Evaluation and Deployment: Examine the influence of predicted occupancy data on the accuracy of energy consumption predictions. This analysis is critical for assessing the added value of occupancy data and enhancing the precision of predictive models, thereby improving the basis for energy management strategies.
3. Research Framework
- Fault Detection Systems: The deployed energy prediction model could be used to analyze the values of the building energy consumption and then analyze the predicted values to detect any discrepancies within the HVAC input data that would result in an error. Examples include but are not limited to the following:
- Errors within the rooftop unit (RTU) outdoor airflow;
- Errors within the RTU supply air temperature;
- Errors within the RTU total supply airflow.
- Load Shape Analysis: The model can analyze and predict the energy consumption for similar buildings within the area under different conditions, such as summer overheating periods, when buildings tend to consume more power for their HVAC systems. Power-supplying organizations can then incorporate the model to prepare to meet their clients’ needs.
- Model Predictive Control: The model can be fitted with a model predictive control that will be trained on the history of the building to learn how the different values for the predictors affect the target variable (energy) and then start to take actions by modifying the set-pints for the HVAC system such as the airflow values and the temperature setpoints that would result in lower energy consumption without affecting the occupant’s comfort. Compared to rule-based control (RBC), MPC is a very efficient system that has recently gained interest in enhancing building energy efficiency.
4. Development of the Energy Prediction Framework: A Case Study Approach
4.1. Data Understanding
4.2. Data Preparation
- Initial Selection:
- Features identified by PCA and DTR were combined to form an initial set of candidate features.
- Model Training and Evaluation:
- Using the initial set of features, a baseline model was trained. Performance metrics such as R2 and RMSE were calculated to evaluate the model’s accuracy.
- Iterative Refinement:
- In each iteration, the subsets of features were systematically removed or added based on their contribution to the model’s performance. The model was retrained with each new subset of features. Features that did not contribute significantly to improving R2 or reducing RMSE were eliminated. This iterative process continued until no significant improvements in model performance were observed, ensuring the most valuable and non-redundant features were retained.
- Validation with Pearson Correlation Analysis:
- Pearson correlation analysis was conducted on the selected features to ensure they were not collinear. This step was crucial to eliminate multicollinearity, which could impair model performance and interpretability.
4.3. Building Energy Modelling
- MLR: Establishes a linear relationship between predictors and the target value by fitting a linear equation to observed data.
- Polynomial Regression (PR): Extends MLR by employing polynomial equations of varying degrees to capture non-linearity in the data. This approach is useful when the relationship between the predictors and the target is non-linear but can be approximated by a polynomial.
- DTR: Uses a tree-based approach where the data are recursively split into subsets based on the feature that provides the highest information gain. The splitting criterion can be based on metrics like the Mean Square Error.
- Random Forest (RF): An ensemble method that combines multiple decision trees to improve predictive performance and robustness. The final prediction is typically the average (for regression) of the predictions from all individual trees.
- Support Vector Regression (SVR): This method defines the best hyperplane that maximizes the margin between the data points and the hyperplane, with the goal of minimizing prediction errors within a specified tolerance.
- ANNR: Consists of interconnected neurons organized in layers, where each neuron processes inputs and delivers outputs based on an activation function.
5. Results and Discussion
5.1. Feature Selection
5.2. Occupancy Prediction
5.3. Building Energy Prediction
5.3.1. Energy Prediction with Actual Occupancy
5.3.2. Energy Prediction with Predicted Occupancy
5.3.3. Error Propagation
- Data Extraction: The data from May to July 2018 for occupancy and Wi-Fi was extracted.
- Occupancy Prediction: The occupancy was predicted based on Wi-Fi connections using a DTR model.
- Dataset Merging: The predicted and actual occupancy datasets were merged with the energy predictors’ datasets, resulting in a relatively small dataset.
- Model Training and Testing: Two different Random Forest energy prediction models were trained and tested using the predicted and actual occupancies.
- Performance Comparison: The R2 and RMSE values for the energy prediction models with actual and predicted occupancy were compared. The energy prediction model with predicted occupancy had an R2 of 0.667 and an RMSE of 8.375, while the model with actual occupancy had an R2 of 0.664 and an RMSE of 8.437. The close values of R2 and RMSE for the predicted and actual occupancy models suggest that the error introduced using predicted occupancy data is minimal. This indicates that Wi-Fi-based occupancy prediction is a viable method for improving the data inputs for building energy models, leading to more accurate energy consumption predictions.
- Data Analysis: The predicted and actual energy consumption versus occupant count and outdoor air temperature are represented in Figure 9. This figure visually represents the relationship between energy consumption, occupant count, and outdoor air temperature. The graph illustrates how energy consumption varies with changes in the number of occupants and how it correlates with outdoor air temperature. The alignment of predicted energy consumption with actual consumption across different occupant counts and varying outdoor temperatures indicates the model’s accuracy in capturing these dynamics. This comprehensive view helps validate the model’s effectiveness in predicting energy consumption based on occupancy and environmental factors.
5.4. Implications of Using Predicted Occupancy Data for Energy Prediction Modelling
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Attribute | Description | Available Period | Time Step (min) |
---|---|---|---|
Avg. merged int. temp. | Indoor air temp. | 18 February–20 December | 10 |
Water supply temp. | Heat pump heating water supply temperature | 18 January–20 December | 1 |
Fan speed | Supply air fan speed | 18 January–20 December | 1 |
Return air temp. | Roof Top Unit return air temp. | 18 January–20 December | 1 |
Air pressure SP | Roof Top Unit air pressure static setpoint | 18 January–20 December | 1 |
Supply air temp. SP | Roof Top Unit supply air temp. setpoint | 18 January–20 December | 1 |
Supply fan speed | Roof Top Unit supply fan speed | 18 January–20 December | 1 |
Mixed air temp. | Roof Top Unit mixed air temp. | 18 January–20 December | 1 |
Outdoor air temp. | Roof Top Unit outdoor air temp. | 18 January–20 December | 1 |
Return fan speed | Roof Top Unit return fan speed | 18 January–20 December | 1 |
Total energy consumption | Total electricity loads (miscellaneous, lighting, and HVAC) | 18 January–20 December | 15 |
Occupancy | Total occupant counts | 18 May–19 February | 1 |
Cooling SP | Cooling temp. setpoint | 18 September–20 December | 5 |
Heating SP | Heating temp. setpoint | 18 September–20 December | 5 |
RTU supply air temp. | Roof Top Unit supply air temp. | 18 January–20 December | 1 |
RTU filtered supply air flow rate | Roof Top Unit filtered supply air flow rate | 18 January–20 December | 1 |
RTU outdoor air flow rate | Roof Top Unit outdoor air flow rate | 18 January–20 December | 1 |
Air temp. | Outdoor air temp. | 18 January–20 December | 15 |
Relative humidity | Outdoor air relative humidity | 18 January–20 December | 15 |
Solar radiation | Outdoor solar radiation | 18 January–20 December | 15 |
Wi-Fi connection | Total Wi-Fi connection counts | May–18 July February–20 December | 5 |
Model | RMSE | R2 |
---|---|---|
DTR | 9.53 | 0.790 |
MLR | 9.54 | 0.789 |
ANNR | 9.43 | 0.794 |
Model | RMSE | R2 Split Validation | R2 Cross-Validation | R2 Grid Search |
---|---|---|---|---|
RF | 4.29 | 0.829 | 0.83 | 0.85 |
SVR | 4.33 | 0.825 | 0.83 | 0.85 |
DTR | 5.84 | 0.68 | 0.69 | 0.77 |
MLR | 7.34 | 0.5 | 0.5 | 0.5 |
PR | 4.56 | 0.8 | 0.51 | 0.8 |
ANNR | 4.22 | 0.836 | 0.83 | - |
Model | RMSE | R2 Split Validation | R2 Cross-Validation | R2 Grid Search | Computation Time |
---|---|---|---|---|---|
RF | 3.78 | 0.897 | 0.9 | 0.92 | 2 h 14 min |
SVR | 4.13 | 0.842 | 0.85 | 0.85 | 10 min 23 s |
DTR | 5.85 | 0.683 | 0.69 | 0.77 | 9 s |
MLR | 7.34 | 0.5 | 0.5 | 0.5 | 6 s |
PR | 4.56 | 0.807 | 0.51 | 0.79 | 1 min 53 s |
ANNR | 3.97 | 0.887 | 0.894 | - | 21 min 25 s |
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Elkabalawy, M.; Al-Sakkaf, A.; Mohammed Abdelkader, E.; Alfalah, G. CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors. Sustainability 2024, 16, 7249. https://doi.org/10.3390/su16177249
Elkabalawy M, Al-Sakkaf A, Mohammed Abdelkader E, Alfalah G. CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors. Sustainability. 2024; 16(17):7249. https://doi.org/10.3390/su16177249
Chicago/Turabian StyleElkabalawy, Moaaz, Abobakr Al-Sakkaf, Eslam Mohammed Abdelkader, and Ghasan Alfalah. 2024. "CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors" Sustainability 16, no. 17: 7249. https://doi.org/10.3390/su16177249