Abstract
Energy metering has gained popularity as conventional meters are replaced by electronic smart meters that promise energy savings and higher comfort levels for occupants. Achieving these goals requires a deeper understanding of consumption patterns to reduce the energy footprint: load profile forecasting, power disaggregation, appliance identification, startup event detection, etc. Publicly available datasets are used to test, verify, and benchmark possible solutions to these problems. For this purpose, we present the BLOND dataset: continuous energy measurements of a typical office environment at high sampling rates with common appliances and load profiles. We provide voltage and current readings for aggregated circuits and matching fully-labeled ground truth data (individual appliance measurements). The dataset contains 53 appliances (16 classes) in a 3-phase power grid. BLOND-50 contains 213 days of measurements sampled at 50kSps (aggregate) and 6.4kSps (individual appliances). BLOND-250 consists of the same setup: 50 days, 250kSps (aggregate), 50kSps (individual appliances). These are the longest continuous measurements at such high sampling rates and fully-labeled ground truth we are aware of.
Highlights
Background & SummaryElectrical energy metering (EEM) has experienced an influx of research activity in recent years due to the shift from mechanical to electronic metering technology
Metering devices used for measuring electrical energy consumption (EEC) and billing consumers are subjected to increased scrutiny over accuracy and reliability
EEC profiles can be generated in smaller time intervals, since smart meters allow for automated meter readings
Summary
Electrical energy metering (EEM) has experienced an influx of research activity in recent years due to the shift from mechanical to electronic metering technology. Recent studies into the psychological effects of EEM feedback have shown that saving energy and actively managing one's EEC requires frequent feedback over long periods, ideally with an appliancespecific breakdown[1] This requires a significant investment in metering hardware, infrastructure, and reliable communication channels to collect the data from a fleet of smaller meters. Large appliances (e.g., space heating, HVAC, washing machines, etc.) are being targeted first to achieve an immediate reduction in EEC since households typically contain a manageable number of them These devices are easier to detect than multiple smaller ones, most datasets use measurement intervals of 1 sample per second (Sps), 1 min, or lower.
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