Abstract
In order to reduce the electricity consumption in our homes, a first step is to make the user aware of it. Raising such awareness, however, demands to pinpoint users of specific appliances that unnecessarily consume electricity. A retrofittable and scalable way to provide appliance-specific consumption is provided by Non-Intrusive Load Monitoring methods. These methods use a single electricity meter to record the aggregated consumption of all appliances and disaggregate it into the consumption of each individual appliance using advanced algorithms usually utilizing machine-learning approaches. Since these approaches are often supervised, labelled ground-truth data need to be collected in advance. Labeling on-phases of devices is already a tedious process, but, if further information about internal device states is required (e.g., intensity of an HVAC), manual post-processing quickly becomes infeasible. We propose a novel data collection and labeling framework for Non-Intrusive Load Monitoring. The framework is comprised of the hardware and software required to record and (semi-automatically) label the data. The hardware setup includes a smart-meter device to record aggregated consumption data and multiple socket meters to record appliance level data. Labeling is performed in a semi-automatic post-processing step guided by a graphical user interface, which reduced the labeling effort by 72% compared to a manual approach. We evaluated our framework and present the FIRED dataset. The dataset features uninterrupted, time synced aggregated, and individual device voltage and current waveforms with distinct state transition labels for a total of 101 days.
Highlights
The United Nations has outlined 17 Sustainable Development Goals [1] for 2030.Related to the production and consumption of electric energy are three of them: stop global warming by clean energy in sustainable cities.One important step to achieve these goals is to reduce the electricity consumption in our homes
We identified different shortcomings of existing datasets: (1) Larger time periods in which no data or only a part of the data are available (REDD, Electricity Consumption and Occupancy (ECO), UK Domestic Appliance-Level Electricity dataset (UK-DALE)). (2) Relative low sampling rate for appliance level data (REDD, UK-DALE, ECO, Almanac of Minutely Power dataset (AMPds)) or no appliance level data at all (BLUED). (3) Missing information about the time and type of appliance events (REDD, ECO, UK-DALE, Building-Level Office eNvironment Dataset (BLOND), AMPds)
We proposed a set of challenges which need to be addressed to record datasets which can be used to evaluate a wide variety of electricity related algorithms
Summary
The United Nations has outlined 17 Sustainable Development Goals [1] for 2030.Related to the production and consumption of electric energy are three of them: stop global warming by clean energy in sustainable cities.One important step to achieve these goals is to reduce the electricity consumption in our homes. Energy monitoring and ’eco-feedback’ techniques have proven to help by raising the awareness of an unnecessary electricity consumption of a particular device. These techniques can be combined with demand-side flexibility to schedule their usage, so that mostly renewable energy is used. If the feedback is provided appliance-wise, they spotted that the savings are up to an average of 13.7 %. These savings are achieved by raising the user awareness
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