## 3 State of the Art
### Data Preprocessing Techniques  
Preprocessing data before using it for HVAC model training is essential due to the inherent complexity and variability of the system data, which are typically composed of time-series sensor information. Proper preprocessing improves data quality, facilitates feature extraction, and enhances the model's ability to detect patterns. Key preprocessing techniques used in HVAC modeling are reviewed here.  
The presence of missing data in HVAC systems can result from sensor failures or communication issues. Several techniques exist for dealing with missing values, each varying in complexity and suitability depending on the nature of the data. Common methods for handling missing data include mean or median imputation, where missing values are replaced by the central tendency of the available data. For more sophisticated imputation, techniques such as kNN [29] or MICE [30; 31] are employed, which consider the correlations between variables to fill in missing values more accurately.  
Outliers in HVAC data can significantly affect the performance of predictive models. Various methods, such as Z-score and IQR, are used to detect and remove data points that deviate from the expected range. More advanced methods like IF [32] or LOF [33] are also utilized to handle outliers in large datasets where traditional methods may be less effective.  
HVAC data is often noisy due to fluctuations in sensor readings. Smoothing techniques, such as Gaussian filtering [34] or moving averages, are applied to time-series data to reduce noise and reveal underlying trends. This enhances model accuracy by providing cleaner features.  
Data normalization is essential to ensure that different features have comparable scales, preventing any single feature from disproportionately influencing model results. Common techniques include Min-Max scaling and standardization.  
High-dimensional HVAC datasets, particularly those with many sensors or features, may suffer from issues such as overfitting and computational inefficiency. PCA [35] is commonly used for dimensionality reduction, helping to retain the most significant features while discarding less informative ones. t-SNE [36] and UMAP [37] are also employed for visualizing high-dimensional HVAC data, though they are less commonly used for direct model training.  
Preprocessing is a standard practice in machine learning; the following are examples of papers that apply a variety of the explained techniques. [PERSON] and [PERSON] [38] applied data cleaning and normalization for neural network control, while [PERSON] et al. [39] used clustering for short-term temperature and CO\({}_{2}\) predictions. Advanced methods, including weight clustering ([PERSON] et al. [40]) and LSTM-based interpolation ([PERSON] et al. [41]) improve load predictions and multistep forecasting. [PERSON] et al. (2022) [42] are the first authors to investigate the influence of data preprocessing and selection on the accuracy of HVAC energy consumption models. Their study emphasizes common preprocessing practices such as data imputation, resolution processing, normalization, outlier detection, and smoothing. Despite this contribution, the role of preprocessing, feature selection, and extraction remains underexplored. This work seeks to address this gap by establishing a methodology to identify key variables and define new ones based on expert knowledge and physics equations. By reducing the dimensionality of features used in data-driven models, the objective is to simplify the process while enhancing the accuracy of temperature and energy consumption forecasts for HVAC systems.