**5. Conclusions**

The operation of smart electrical power grids has become unimaginable without the opportunity to capture the status of grid-connected consumers in real-time and at fine resolution. Processing smart meter data has traditionally been centered around use cases that benefit the operations of electricity providers and the stability of the power grid [5]. The range of services that are tailored to the needs of end-customers is still comparably small. In this review paper, we present and discuss the range of use cases that are enabled through the collection of smart meter data but primarily benefit the consumers of electrical energy. We believe that three major preconditions are crucial for the long-term establishment of user-centric service provision. First, smart meters and the corresponding data processing mechanisms must be capable of reporting accurate information. They must undergo continuous improvements in order to extract the information content to the fullest extent possible. Second, adequate measures must be provided to protect user privacy. Established methods to provide secure networking must be combined with meaningful local preprocessing steps to remove sensitive features before data leave the customers' premises. Third, not all services apply to all users in the same way. A dedicated ecosystem, such as an "app store" for energy-based services (similar to the proposition in [164]), thus represents a viable option to allow consumers to individually subscribe to their desired services and understand the ensuing privacy implications. The range of user-centric data analysis methods, as surveyed in this work, can then be executed either locally or with the help of remote execution environments. A corresponding ecosystem will ultimately make it possible for both developers and providers of smart meter data processing methods to easily offer novel services, and simultaneously lower the barrier for customers to consume these services and avail of their benefits.

**Author Contributions:** Conceptualization, A.R. and L.P.; methodology, A.R., L.P., B.V. and A.F.; formal analysis, L.P. and A.F.; investigation, B.V. and A.F.; resources, L.P.; data curation, B.V.; writing— original draft preparation, A.R. and L.P.; writing—review and editing, B.V.; visualization, B.V. and A.R.; supervision, A.R. and L.P.; project administration, L.P. and A.R.; and funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by Deutsche Forschungsgemeinschaft gran<sup>t</sup> No. RE 3857/2-1 and by the Portuguese Foundation for Science and Technology grants CEECIND/01179/2017 and UIDB/50009/2020.

**Conflicts of Interest:** The authors declare no conflict of interest.
