Abstract
Smart meters have become a core part of the Internet of Things, and its sensory network is increasing globally. For example, in the UK there are over 15 million smart meters operating across homes and businesses. One of the main advantages of the smart meter installation is the link to a reduction in carbon emissions. Research shows that, when provided with accurate and real-time energy usage readings, consumers are more likely to turn off unneeded appliances and change other behavioural patterns around the home (e.g., lighting, thermostat adjustments). In addition, the smart meter rollout results in a lessening in the number of vehicle callouts for the collection of consumption readings from analogue meters and a general promotion of renewable sources of energy supply. Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can further support a reduction in energy usage. This research focuses on modelling trends in domestic energy consumption using density-based classifiers. The technique estimates the volume of outliers (e.g., high periods of anomalous energy consumption) within a social class grouping. To achieve this, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Local Outlier Factor (LOF) demonstrate the detection of unusual energy consumption within naturally occurring groups with similar characteristics. Using DBSCAN and OPTICS, 53 and 208 outliers were detected respectively; with 218 using LOF, on a dataset comprised of 1,058,534 readings from 1026 homes.
Highlights
Smart meters offer a sustainable flow of energy consumption data between consumers and utility companies
A significant amount of research exists on how the data collected from the smart meters can be used to detect energy usage patterns in residential homes via user profiling techniques [1]
We demonstrate how independent user models, when combined, can be used to identify anomalous energy consumption points within granular datasets
Summary
Smart meters offer a sustainable flow of energy consumption data (as well as time stamp and user identification) between consumers and utility companies. Smart meters offer a more resilient energy grid management process. A significant amount of research exists (some of which is presented in this paper) on how the data collected from the smart meters can be used to detect energy usage patterns in residential homes via user profiling techniques [1]. 800 million in total [5] This advanced IoT network of smart meters provides a novel gateway into the home, opening up emerging areas of innovative research including human activity profiling, home automation, load management, Non-Intrusive Load Monitoring (NILM), appliance efficiency monitoring, and energy theft [12]. The consumption readings can be filtered based on categories such as social class, age, gender, opinion on smart meters, cooker type, etc
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