## 1 Introduction
In contrast, other feedback studies could not find a clear link between household-level characteristics and price effectiveness [22]. In addition to the influence of electricity prices on absolute electricity consumption, the effect of building characteristics and socio-demographic information significantly influence energy demand behavior. Testing TOU demand behavior in Germany, [PERSON] and [PERSON] [23] find that the size of a household's building positively influences energy consumption and that the number of appliances positively influences the electricity consumption. The number of occupants of a household, the building size of a household, the building type, and the number of bedrooms is found to positively influence electricity consumption [24]. A study characterizing domestic electricity consumption patterns in Ireland found that the number of occupants, the building size and the building type positively influence residential electricity consumption [25]. Contradictory findings have been reported as well, for example the authors of [26] investigate determinants of residential electricity consumption, finding that the number of occupants and the building size are positively associated with electricity consumption, while the building type and the building age are not associated with electricity consumption. A study investigating short- and long-term price elasticity [27] found that income positively influences peak and off-peak residential electricity consumption, while the household size does not show any significant influence. Whereas the previously mentioned studies investigate the influence of household attributes in a flat or TOU pricing environment, little work has been done in dynamic RTP settings, let alone comparing tariff schemes. [PERSON], [PERSON] and [PERSON] [28] find that in a dynamic tariff scheme, only building type shows a significant influence on electricity usage, but they do not compare the variety of household attributes to other tariff schemes. Our study is one of the first to analyze for both TOU and RTP, and validate the efficient targeting for utility companies in terms of these characteristics. An overview of related literature can be found in Table 1.  
With the growth of home energy management systems and smart meters, the volume, frequency and variety of information is growing exponentially [29]. Business intelligence and machine learning techniques can provide utilities and energy-market-related companies with demand forecasts and customer usage patterns to support data-driven decision making. In such a high-dimension space with high frequent multi-variable data as the above context, developing effective clustering methods for customer patterns is a challenging problem, due to the curse of dimensionality [30]. We apply spectral relaxation by a Principal Component Analysis (PCA) to find a meaningful representation of the intrinsic dimensionality of the data and perform a k-means clustering analysis in the projected space, to analyze distinct customer group behavior from the natural experiment. This method has become one of the important machine learning algorithms for learning consumer behavior [31], particularly for energy demand forecasting [32; 33]. Our paper makes several contributions to both dynamic pricing literature and the design of home energy management systems. First, the study shows that time capabilities play a significant role when examining usage behavior in dynamic price settings. By looking at each hour independently, we aim to identify specific times during the day, in which the households are adapting their behavior more rigorously in order to take advantage of varying price levels. We confirm different daily demand profiles for both tariff schemes and find evidence for a significant influence of household attributes. These attributes influence our different household clusters, which have varying capabilities to react to the respective dynamic pricing scheme.