# 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
Several potential solutions can be considered to alleviate the issues brought on by model complexity in energy efficiency. A strategy is to balance model complexity and simplicity while considering the trade-off between accuracy and interpretability. Without significantly affecting performance, the model can be made simpler by choosing pertinent features and using interpretable techniques. Incorporating domain knowledge and expert views can also direct the model-development process and increase the model's applicability in real-world situations. Interpretability: Model interpretability in energy efficiency relates to the ability to understand and explain the decision-making processes of ML models employed in this domain. It solves the issue of complicated ML algorithms being black boxes, which makes it difficult to see how predictions are made, even while the models produce accurate results. For stakeholders, including building owners, facility managers, and energy professionals, to gain insights into the variables influencing energy consumption and the motivations behind particular recommendations or actions suggested by the ML models, interpretability is crucial in the energy efficiency domain. It promotes trust, facilitates decision-making, and identifies places where energy efficiency techniques can be strengthened [113]  
For applications involving energy efficiency, several methodologies and techniques have been suggested to improve model interpretability. These consist of the following:  
* Feature importance analysis: It determines the factors or features that have the most influence on the ML model's judgment. Techniques like LIME (Local Interpretable Model-agnostic Explanations), SHAP (Shapley Additive explanations), or permutation importance can be used. * Creating streamlined rule-based models: They mimic the behavior of complicated ML models. These models use transparent explanations and rules that can be read by humans to describe the decision-making process. * Visualizations: To help comprehend the connection between input data and output predictions, visual representations of model predictions are presented, such as heatmaps, decision trees, or partial dependence plots. * Sensitivity analysis: Analyzing how changes in inputs affect the model results to determine how sensitive and robust the ML model is. It aids in locating crucial details and comprehending how the model behaves in various situations. Model-agnostic techniques: Making use of methods that may be used to interpret the outcomes of any ML model without requiring model-specific information. This covers techniques like surrogate models, LIME, or SHAP values. By utilizing these interpretability techniques, stakeholders can gain a deeper understanding of how ML models recommend energy efficiency, comprehend the underlying causes of energy consumption, validate the models' performance, and make informed decisions based on the provided explanations. * Diverse research objects: Although the data mining capabilities of ML models for building energy prediction problems can be effectively demonstrated through various research objects, heterogeneous research objects may not be advantageous for deploying ML models in practical implementations. Which energy prediction issues are the most pressing and accessible to ML technology solutions is still up for debate among the studies that have already been conducted. In other words, it is still unclear whether time-scale and prediction items are essential for enhancing building energy performance. However, the concept for the rapid development of ML and its widespread applicability in any discipline is that it can lead to ground-breaking discoveries. Finding appropriate application opportunities is more important than the model's capacity to fit the data for that field. A work object's non-uniformity makes it harder to compare and choose ML algorithms and non-standardized data structures, ultimately hindering the creation of a general ML-based building energy management modeling solution.