**1. Introduction**

After the invention of electricity meters over a century ago, billions of such devices have been installed worldwide [1]. They are found in private households, commercial buildings, industrial sites, and all other domains that require electrical energy consumption to be tracked. Initially realized as rotating-disc meters (also known as Ferraris meters) with mechanical displays, transferring the actual energy consumption data used to be a labor-intensive manual process. However, with the rise of digital metering devices, so-called smart meters, the collection of electrical power consumption at much more finegrained spatial and temporal resolutions has become possible. The digital communication interface to report the collected data represents a major advancement over rotating-disc meters in particular. As a result, meter data have started to become available at previously unimaginable temporal resolutions on the order of seconds to minutes, on building- or even apartment-level. Moreover, the resultant digital data can be easily transmitted to online data centers for storage and further data processing. This opens up unprecedented opportunities to analyze, compare, and combine such data and provide novel energy-based services to customers and grid operators alike [2].

**Citation:** Völker, B.; Reinhardt, A.; Faustine, A.; Pereira, L. Watt's up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective. *Energies* **2021**, *14*, 719. https:// doi.org/10.3390/en14030719

Received: 29 December 2020 Accepted: 26 January 2021 Published: 30 January 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Numerous research activities have accompanied the global roll-out of smart meters [3], seeking to exploit the information content of the collected data to its fullest extent. By leveraging and combining signal processing techniques from a wide range of domains (digital signal processing, stochastic analysis, artificial intelligence, and many others), various indicators and identifying features can be detected and extracted from smart meter data. They allow for the realization of plentiful use cases that benefit consumers, utility companies, or other stakeholders. It is noteworthy, however, that existing works have focused mainly on smart meter data analytics methods to the benefit of the power grid as a whole [4,5]. In contrast to this application domain, equally grea<sup>t</sup> potential lies in the provision of user-centric services based on electrical consumption data. We dedicatedly survey such use cases in this work, not least because we anticipate many more services that provide direct benefits to electricity consumers to be developed in the near future.

This review of the state of the art in smart meter data analytics applications is targeted to be a concise introduction that seeks to provide an overview of the range of user-centric applications for smart meter data as well as highlighting promising future research avenues in this domain. It is organized as follows. Section 2 sets the definition of smart meter data used in this paper and highlights frequently used data (pre)processing steps. We survey consumer-centric applications based on smart meter data in Section 3 including the provision of electricity related user feedback, the recognition of patterns or anomalies, the recognition of flexible loads, vital improvements in single home demand forecasting, and finally the comparison and correlation of consumers based on their load profiles. Section 4 discusses the most widely encountered obstacles in developing customer-centric smart meter data services such as missing standardization, mediocre-performing algorithms, and privacy concerns. To surmount these obstacles, we formulate the corresponding research challenges and finally conclude this review paper in Section 5.

#### **2. Smart Meter Data Collection and Preprocessing**

Metering the consumption of primary energy is commonplace and an everyday experience for most people. When refueling the storage tanks of oil- or gas-powered central heating systems or vehicles powered by combustion engines, measuring fuel quantities is ubiquitous to be billed only for the amount added. Consumption metering has also manifested itself for commodities beyond fuels, such as running water, district heating, or pressurized air. However, none of these fields has seen the same enormous increase in data analytics research as the field of electrical power consumption monitoring. In fact, besides a single work on interpreting natural gas consumption [6], only the evaluation of water flows has seen scientific consideration in related works [7–11], primarily seeking to infer user activities based on the corresponding water demands. The reason for the surge of electrical consumption analytics is simple: With the rise of smart meters, electrical consumption data have become available in unprecedented temporal and spatial resolutions. This not only makes longitudinal analyses much easier to conduct, but the high penetration of the building stock with smart meters has also created the foundation to run data analytics at scale. The large variety of electrical consumers [12,13], coupled with their frequent use in everyday activities and the ensuing potentials to save energy, makes them viable candidates for analysis. Before surveying possible use cases and their practical implications in the following section, however, let us first revisit the definition of smart meter data and delineate them from other ways of consumption measurements in electrical power grids.

As shown in Figure 1, smart meters are located at the entry-point of a building's electrical grid connection. All power flows between the (smart) power grid and the appliances in the (smart) home can be captured at this point, thus smart meters can effectively lead to benefits on both sides. Their primary use case lies in billing consumers for the exact amount of electrical energy taken from the power grid or balancing between consumption and generation of *prosumers*, i.e., grid-connected entities with local generation facilities, respectively. Smart meters are thus distinct from both customer-side monitoring systems, such as circuit-level or even plug-level power monitors, which rarely exhibit the same

accuracy as smart meters but rather serve as data sources for smart home installations or Building Management Systems (BMS). Likewise, monitoring devices exist in transmission and distribution grids, ye<sup>t</sup> their data can generally not be unambiguously attributed to a single customer. Coupled with their generally smaller number when compared to the scale at which smart meters have been rolled out, we also exclude such grid-level monitors (such as phasor measurement units) from our analysis in this paper. We specifically wish to highlight, however, that the presented data processing mechanisms and correspondingly enabled use cases can likely also find application on such devices or smart meters for quantities beyond electrical energy.

**Figure 1.** Location of smart meters within the electrical power grid (icons by Icon Fonts; CC BY 3.0).

Smart electricity meters represent the state-of-the-art solution to collect, process, and forward load information to all stakeholders involved. Through direct connections to the Internet, or indirect connection using smart meter gateways [14], access to metered data is ubiquitously possible. Incentive schemes and policymakers in many countries furthermore contribute to the increasing market penetration of smart meters. This enables numerous user-centric use cases beyond billing, which we survey and categorize in Section 3. Before documenting how the full potential of smart meter data can be unleashed, we would like to note that the enablement of these use cases frequently relies on data preprocessing steps to isolate characteristic features from the stream of raw measurements provided by smart meters.
