# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 8 Limitation, Challenges, and Opportunities of Existing Solutions
### Practical Challenges in Applying ML for Building Energy Efficiency
For example, if certain sensors or meters are not installed in all buildings or energy systems, it can result in an imbalance in the collected data. The presence of data imbalance in the energy efficiency field has several implications:
* Biased model performance: ML models developed on imbalanced datasets sometimes exhibit a bias towards the majority class due to the greater number of samples available for learning. This may lead to suboptimal performance in identifying and forecasting infrequent energy-saving occurrences or anomalies. * Decreased model generalization: Imbalanced datasets can lead to models that struggle to generalize well to real-world scenarios. The lack of sufficient data for underrepresented classes can limit the model's ability to capture their characteristics and make accurate predictions in practical applications. * Evaluation challenges: Traditional performance measurements like accuracy may be deceptive when addressing unbalanced datasets. Models that merely forecast the predominant class can attain elevated accuracy, although they neglect the objectives of energy efficiency. To address data imbalance in the energy efficiency field, several strategies can be employed. * Data resampling: This process entails either oversampling the minority class or undersampling the majority class to establish a more balanced dataset. Methods such as random oversampling, the Synthetic Minority Over-sampling Technique (SMOTE), or Tomek connections can be utilized to accomplish this rebalancing. * Class weighting: Providing varying weights to different classes during model training might mitigate the imbalance. By assigning greater significance to the minority class, the model can enhance its focus on its patterns and augment its predictive efficacy. * Ensemble methods: Ensemble approaches, like boosting and bagging, can amalgamate several models trained on various subsets of imbalanced data. This can enhance the representation of the minority class and elevate overall model efficacy. * Anomaly detection: Instead of considering the imbalanced classes as standard data, specialized anomaly detection algorithms may be employed to discover and concentrate on infrequent occurrences or energy-saving abnormalities. These algorithms can be specially trained on the minority class to identify and emphasize such instances. * Model complexity: Model complexity describes the degree of sophistication and complexity of a model employed in energy efficiency. It is an indicator of how many variables, parameters, and interactions are included in the model to effectively represent the system in real life. Complex models can capture complicated linkages and offer granular insights, but they also come with difficulties in terms of interpretability, robustness, and processing requirements. Model complexity in the context of energy efficiency can result from various sources. To capture the numerous aspects of energy consumption in buildings, several input factors or variables must be considered, including weather conditions, occupancy patterns, building characteristics, and operational parameters. To handle intricate patterns and non-linear correlations within the data, sophisticated algorithms or techniques, like deep learning or ensemble methods, may also be used [112]. Model complexity has a variety of effects on the subject of energy efficiency. On the one hand, complicated models may be able to make forecasts that are more accurate and reveal untapped information that will improve energy efficiency techniques and decision-making. Increased model complexity, nevertheless, might sometimes present difficulties. It might be impractical for real-time applications or large-scale implementations because it might require much computer power and take longer to train. Furthermore, complicated models can be difficult to understand, reducing their transparency and making it more difficult to comprehend the fundamental processes underpinning energy efficiency.