Abstract
Household electricity consumption can be broken down to appliance end-use through a variety of methods such as modelling, sub-metering, load disaggregation or non-intrusive appliance load monitoring (NILM). We advance and complement this important field of energy research through an innovative methodology that characterises the energy consumption of domestic life by making the linkages between appliance end-use and activities through an ontology built from qualitative data about the household and NILM data. We use activities as a descriptive term for the common ways households spend their time at home. These activities, such as cooking or laundering, are meaningful to households’ own lived experience. Thus, besides strictly technical algorithmic approaches for processing quantitative smart meter data, we also draw on social science time use approaches and interview and ethnography data. Our method disaggregates a households total electricity load down to appliance level and provides the start time, duration, and total electricity consumption for each occurrence of appliance usage. We then make inferences about activities occurring in the home by combining these disaggregated data with an ontology that formally specifies the relationships between electricity-using appliances and activities. We also propose two novel standardised metrics to enable easy quantifiable comparison within and across households of the energy intensity and routine of activities of interest. Finally, we demonstrate our results over a sample of ten households with an in-depth analysis of which activities can be inferred with the qualitative and quantitative data available for each household at any time, and the level of accuracy with which each activity can be inferred, unique to each household. This work has important applications from providing meaningful energy feedback to households to comparing the energy efficiency of households’ daily activities, and exploring the potential to shift the timing of activities for demand management.
Highlights
Energy efficiency and energy conservation are priorities for governments worldwide, and have motivated intensive research over⇑ Corresponding author.the past decade into understanding how energy is consumed, and how to translate that knowledge into meaningful information to enable energy consumers to take responsibility for their energy consumption
Each activity column is traffic-light colour coded: green indicates an activity can definitely be inferred; red indicates that an activity is not inferable from the current data; amber refers to an activity that can possibly be inferred if readings are available from individual appliance monitors (IAM) since relevant appliances cannot be reliably inferred by the Non-Intrusive appliance Load Monitoring (NILM) algorithm
The input to the activity inference algorithm comprises the appliance label, when the appliance was switched on, when it was switched off, and estimated electricity consumption obtained from the sensor, i.e., disaggregation via NILM or IAMs as well as the ontology
Summary
Energy efficiency and energy conservation are priorities for governments worldwide, and have motivated intensive research over. We propose an autonomous activity inference algorithm that characterises the energy consumption of domestic life by making the linkages between appliance end-use and activities through an ontology built from qualitative data about the household and disaggregated electricity consumption data. This comprises four key steps, namely capturing quantitative and qualitative data, disaggregating aggregate smart meter measurements via non-intrusive load monitoring where appliance-level measurements are not available, building a household-specific ontology from qualitative data, and making inferences, automatically, via an activity recognition algorithm using the ontology and disaggregated appliance end-use data.
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