# 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  
A study of the causes, effects, and possible solutions to the practical challenges faced in implementing ML for building energy efficiency will aid researchers and practitioners in understanding the key factors that obstruct the application of ML technology in building energy efficiency practices. * Limited techniques and resources for data collection: Any ML-based building energy prediction model must commence with data collection [109]. The collection of building energy management data currently faces two major challenges: constraints in data collection methodologies and the lack of a cohesive data collection platform. As sensor technology progresses, an increasing volume of building information can be precisely monitored. The U-factor (thermal transmittance) of the building envelope and the light transmittance of the glass curtain wall are critical design elements that directly influence a building's energy efficiency. Nonetheless, quantifying them continues to pose a challenge [1]. Collecting data for building energy modeling is often arduous and time-intensive, similar to many other domains [110]. The literature reveals that there is presently no cohesive data-collection infrastructure enabling researchers to effectively and rapidly acquire the necessary data for building energy prediction. Essential data is routinely collected for ongoing research from many databases [111]. To gather the requisite data, they may occasionally need to devise a system. Consequently, data collection is significantly more challenging than model training. Building energy modeling data collection is frequently laborious and time-consuming, like in many other fields [110]. The literature indicates that there is currently no integrated data-collecting platform that allows researchers to efficiently and swiftly gather all of the data required for building energy prediction. The necessary data is frequently gathered for existing investigations from several databases [111]. To collect the necessary data, they occasionally even need to design a system. As a result, data gathering is much more difficult than model training. This multi-source data collection method is generally ineffective in practical applications due to privacy concerns. The collection of information regarding building energy is hindered by the insufficient integration of data resources, thereby complicating the widespread application of ML models in building energy management. Data quality and collection challenges can be effectively addressed by utilizing multi-source data fusion techniques, enhancing data governance frameworks, and advancing sensing technology [1]. * Data imbalance: Data imbalance in the energy efficiency field refers to a situation where data distribution across different classes or categories is significantly skewed. This means that one or more classes have a much smaller representation in the dataset than others. This issue is particularly relevant in energy efficiency applications where certain energy-saving behaviors, events, or anomalies are infrequent. Data imbalance in the energy efficiency domain can occur due to several reasons:Unbalanced energy consumption patterns: Energy usage patterns in buildings or systems may naturally exhibit imbalance, with certain events or behaviors occurring less frequently. For example, anomalies such as equipment malfunctions or energy wastage incidents may be relatively rare compared to normal operating conditions. * Sampling bias: The data collection process may introduce bias, resulting in imbalanced datasets. For instance, data collection methods that prioritize specific time periods, building types, or energy systems may inadvertently underrepresent certain classes or categories. * Data collection limitations: A limited monitoring infrastructure or incomplete data collection can lead to imbalanced datasets.