## 1 Introduction  
Electricity providers are currently driving deployment of smart electricity meters in a number of households worldwide to collect fine-grained electricity usage data. The changes taking place in the electricity industry require effective methods to provide end users with the feedback on electricity usage which is in turn used by the network operators for formulating pricing strategies, constructing tariffs and undertaking actions to improve the efficiency and reliability of the distribution grid. With high expectations towards smart metering adoption and its influence on households notwithstanding, it is observed that utilization of the information from fine-grained consumption profiles is in its initial stage. This is due to the fact that consumption patterns of individual residential customers vary a lot which is the function of the number of inhabitants, their activity, age and lifestyle [1]. Various techniques for customer classification are discussed in the literature, with the focus on electricity usage behavior of the customers [2; 3; 4; 5]. These works contribute to higher energy awareness by providing the input for demand response systems in homes and supporting accurate usage forecasting on the household level [6; 7; 8]. Recently, a new relevant research stream may be distinguished with the underlying idea to identify important household characteristics and leverage it for energy efficiency. It is focused on the application of supervised machine learning techniques for inferring such household properties as number of inhabitants including children, family type, size of the house, and many other characteristics [9; 10]. In particular, this work relies upon the works of [PERSON] et al. and [PERSON] et al. and further it is supposed to enhance the approach by extending the methodology for features selection. Therefore, this paper applies the GCA segmentation approach to derive important features describing electricity usage patterns of the households. The knowledge of the load profiles captured by smart meters might be helpful to reveal relevant household characteristics. These customer insights can be further utilized to optimize the energy efficiency programs in many ways, including with the introduction of flexible tariff plans and enhanced feedback loop [11; 12]. The later one applies to feedback programs that engage households in energy saving behaviors, and helps to recognize what actions inhabitants are undertaking to bring the feedback into energy savings [13]. In particular, the proposed paper enhances methodology for customer classification taking into account historical electricity consumption data captured by a large set of 91 attributes, tailored specially to describe various aspects of behaviors typical for different type of households. Therefore, the scope of the paper is threefold:  
1. Extraction of the comprehensive set of the behavioral features to capture different aspects of household characteristics;
2. Application of grade cluster analysis to identify important attributes to detect distinct consumption patterns of the customers and further, using only a subset of relevant features for classification, to reveal socio-demographic characteristics of the households;
3. Classification of households' properties using three machine learning algorithms and three feature selection techniques. The proposed research fits into the attempt focused on leveraging smart meter data to support energy efficiency on the individual user level. This gives novel research challenges in monitoring usage, data gathering, and inferring from data in a non-intrusive way since customer classification and profiling is methodically sound and offers a variety of potentials for application within the energy industry [14; 15; 16]. In the attempt to reduce electricity consumption in buildings, identification of important features responsible for specific patterns of energy consumption at different customer groups is a key to improving efficiency of available energy usage.