# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
###### Abstract
Typical energy-consumption patterns have been extensively studied using various forecasting techniques. Ref. [10] utilized a spectral clustering algorithm combined with temporal fusion transformers to forecast building energy consumption. This approach enhances the interpretability of the models used. Similarly, ref. [11] presented a new method for seasonal energy consumption-forecasting employing temporal convolutional networks, which effectively capture the seasonal variations in energy use. Identifying atypical energy-consumption patterns is a relatively newer area of research. Studies such as ref. [12] have focused on uncovering unusual consumption behaviors in households. Our study advances this field by combining multiple feature-selection techniques, including the Chi-square test, recursive elimination feature, random forest and fuzzy rough feature selection to identify key determinants of atypical energy usage. This comprehensive approach provides a nuanced understanding of household energy consumption, highlighting deviations that can inform targeted energy-conservation strategies. Feature selection plays a vital role in refining these methods. It is a technique that reduces the data-processing scale by removing irrelevant and redundant features, thereby reducing dimensionality, improving learning accuracy, reducing learning time and simplifying learning results [13; 14]. No single feature-selection method is universally applicable to all datasets [15]. Each dataset possesses unique characteristics that influence the effectiveness of different feature-selection techniques. Consequently, it is essential to evaluate and test various methods to determine the most suitable approach for a given dataset. There are three primary types of feature-selection techniques: filter methods, wrapper methods and embedded methods. Filter methods are computationally efficient because they are independent of the classifier, making them easy and quick to implement [16]. The most common filter methods use statistical measurements to determine correlation or independence between input features and the target variable [17]. Wrapper methods evaluate the features and produce the result simultaneously with a learning model using a learning algorithm, resulting in higher classification accuracy but with the disadvantage of high time complexity [18]. Embedded methods incorporate the classifier's bias into feature selection, producing better classifier performance and greater efficiency since they do not need to evaluate feature sets iteratively [19; 20]. This study aims to address the gap in understanding atypical energy-usage patterns by employing advanced feature-selection techniques to uncover the critical determinants of household energy usage. In our research, we tested several feature-selection methods: the chi-square test to assess the features' independence [17], recursive feature elimination (RFE) with multinomial logistic regression (MLR) [18], random forests (RF) [15] and fuzzy rough sets (FRS) [13; 14]. These techniques help identify the most informative features affecting energy consumption and offer a deeper understanding of consumer behaviors. The dataset used in this study is part of a survey administered to 383 randomly selected households in Brazil, collecting data on monthly electrical power consumption and the characteristics of the households and occupants [21]. This comprehensive dataset provides a robust foundation for analyzing energy-consumption patterns and identifying key determinants. The household energy usage was categorized into three distinct classes: low-, medium- and high-load-consumption profiles. By applying these advanced feature-selection techniques, this research aims to set a new standard in energy conservation, offering a robust template for future studies focused on optimizing residential energy use. The ultimate goal is to empower urban actors to make data-driven decisions for a more sustainable future.