## 4 Conclusions  
Home energy management systems are believed to play a crucial role in efficiently capturing the benefits of DR programs to ensure demand flexibility and peak load reduction. The effectiveness of such programs are, however, largely affected by the willingness of end-users to be involved in such  
Figure 5: Visualization of Five-Cluster Solution of K-Means Analysis Along the Two Strongest Principal Components. Figure 6: Differences in the Relative Electricity Usage (kw/h) across the Identified Clusters. programs. Based upon the individual preferences and beliefs of the customer, not every dynamic pricing program will be as effective for the overall welfare of customer and utility company. This paper, therefore, aims to identify demand patterns of households with the two most-widely used dynamic pricing settings, TOU and RTP pricing. As such, it allows to study a better fit between customer and electricity tariff, allowing for a more optimal use of demand flexibility by the energy management system. Our setting is a natural experiment, involving real-world customers of a national utility company participating via dynamic pricing contracts. In a first step, we find a significant impact of the dynamic pricing scheme on the relative electricity usage of the individual households and find evidence for different demand behavior based upon the individual household attributes. This indicates the need for individual customer tariff targeting, as static building characteristics, such as building age, building size and building type, and demographic characteristics, such as the number of household occupants, make end-consumers act significantly different in the two dynamic pricing schemes. Secondly, we identify distinct groups of customer demand patterns by conducting a spectral clustering analysis. Our results show clusters with clearly distinguishable behavioral electricity usage patterns. Using machine learning in non-convex data-sets such as household electricity demand data, will allow energy management systems to more optimally target customers based upon individual preferences. The study presents several conclusions and implications for exploiting smart home energy management systems. By segmenting households based on their household attributes, we are able to isolate groups that differ in their level of engagement with the respective dynamic pricing scheme. This indicates that home energy management systems do not perform equally over a varying set of households, with respect to reducing and shifting load. Our study provides an initial better understanding of households their willingness to engage with dynamic pricing schemes, indicating that successful implementation of home energy management systems will not only be based upon the respective DR program, but also on the individual household preferences and beliefs. **Acknowledgments:** Special thanks go to [PERSON], Business Development Manager at the Dutch utility company Qurrent Energie, for his guidance and assistance in the experiment. **Author Contributions:** [PERSON], [PERSON] and [PERSON] jointly contributed to the research model and implementation, data analysis and writing of the paper. **Conflicts of Interest:** The authors declare no conflict of interest.