# 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
To address this, it is required to identify the principal building energy efficiency issues that still need to be resolved using ML technology, which requires combining a market demand work with a technical feasibility analysis. * Diverse ML algorithms: A wide range of ML algorithms have been employed in studies of building energy efficiency due to the vigorous advancement of ML. Artificial neural networks (ANNs) [114], Support Vector Machines (SVMs), Multiple Linear Regression (MLR) [115; 116], Extreme Learning Machines (ELMs) [114], deep learning (DL) [117; 118], decision trees (DTs) [119], and random forests (RFs) [120; 121] are among the algorithms employed historically. Recent research indicates that more than 128 ML algorithms have been utilized in various studies [111]. Nevertheless, no flawless algorithm has been identified to date [3]. Although various distinct ML solutions have been developed to tackle the same issue, it has not been proven that any ML strategy consistently surpasses other algorithms in all scenarios [1]. Inconsistent experimental conditions in studies involving real-world building scenarios result in divergence and a lack of generalizability in the findings. The discrepancies in prediction output, data formats, and data volume lead to uneven experimental designs. The lack of an optimal algorithm is further exacerbated by inconsistent evaluation criteria, including the coefficient of variation (CV), mean absolute error (MAE), and computing time. Limited research emphasizes the utilization of diverse evaluation criteria to evaluate the effectiveness of ML systems. The application of ML techniques for enhancing building energy efficiency has been constrained by unique building attributes and human preferences. Sharing data and established evaluation criteria can assist in identifying the most effective algorithms for building energy prediction issues, enabling cross-project comparisons and improving overall efficiency in this domain. * Unstructured data: The lack of a standardized data structure is a significant practical challenge in present research, which is essential for the advancement of ML-based building energy modeling. Data employed for constructing energy models has been aggregated and classified by current research [122] (see Table 9). Researchers often consider the characteristics of prediction objects and data availability when selecting input variables and sample rates. Consequently, the volume and composition of the data differ among studies. The principal obstacle is that the fundamental building energy management challenges for ML model analysis have not yet been established. Unifying the corresponding data structure is problematic due to the variability of the prediction objects [1]. Divergences in data structures hinder the development of a consistent standard for data collection, thereby complicating the comparability of research findings. Consequently, ML-based building energy forecasting models are less prevalent [123]. It is essential to define the primary issues that ML can address and to summarize the pertinent data resources. The data structure must be customized to the particular prediction problem being addressed. * Technology-oriented research paradigm: Finding challenges with considerable application potential is a crucial prerequisite for successful ML applications. These issues should be ones that ML models are likely to be able to resolve and that will have a big or revolutionary impact on their respective fields [1]. The technology-oriented research paradigm, however, encourages researchers to prioritize testing the ML models' capacity to learn from building energy data [3]. In-depth analysis and discussion of the application values of the prediction problems and the applicability of the suggested ML models to building energy management methods are lacking in the research that has resulted. The inconsistent research objects and data formats reported in previous studies are primarily due to this.