Electrical-end-use data from 23 houses sampled each minute for simulating micro-generation systems

Abstract

An improved understanding of the consumption patterns, end-uses, and temporal variations of electrical loads in houses is warranted because a significant fraction of a society's total electricity consumption occurs within residential buildings. In general, there is a lack of high-temporal-resolution data describing occupant electrical consumption that are available to researchers in this field. To address this, new measurements were performed and combined with data emanating from an earlier study to provide a database of annual measurements for 23 houses at a 1-minute resolution that characterizes whole-house, non-HVAC, air conditioner, and furnace fan electrical draws, as well as the draw patterns of some major appliances. All houses were located in Ottawa, Canada. The non-HVAC measurements of this 23-house sample were shown to be in agreement with published estimates for the housing stock. The furnace fan was found to be the most significant end-use. These high-temporal-resolution data of electrical demands in houses can be used by researchers to increase the fidelity of building performance simulation analyses of different micro-generation technologies in residential buildings.

Solving the fair electric load shedding problem in developing countries

“…The performance of the heuristics were assessed in terms of connections and comfort in[32], as in a chore division setting. However, to conform with the results to be evaluated later in this paper, we reproduce the results in[32] in terms of connections and comfort, as in a cake-cutting setting 19. Note that the results presented inTable 3are obtained from solving the fair load shedding problem once 20.…”

“…For this reason, any dataset adapted to the developing country context should be one from which the consumption data of appliances commonly used in developing countries can be extracted. Consequently, the HES [9], RBSA [51], OCTES [50], EEU (known as the Electrical-end-use data) [19] and REFIT [27] datasets are inadequate as they were collected at the submeter level. This leaves us with the dataset from Pecan Street Inc's Dataport [36].…”

Potential use of thermal energy storage for shifting cooling and heating load to off‐peak load: A case study for residential building in Canada

“…They emphasized that most of the consumed energy in the residential building is used for heating and cooling. Johnson and Beausoleil‐Morrison conducted a study for obtaining electricity consumption data from 23 residential buildings in Canada in order to determine the electricity demand profile for entire house, non‐HVAC, air conditioning, and furnace. Kipping and Tromborg modeled and disaggregated hourly electricity consumption by using smart meter data in residential building in Norway.…”

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

“…An equivalent metric was proposed in Kolter and Jaakkola (2012), but this time considering the individual appliance error (IATEAE) rather than the average between all the appliances, which reduces the chance of reporting large errors in certain time slices due to single appliances performing very poorly. (Picon et al, 2016) SustDataED (Ribeiro, Pereira, Quintal, & Nunes, 2016) EEUD (Johnson & Beausoleil-Morrison, 2017) RAE (Makonin, Wang, & Tumpach, 2018) ESHL (Kaibin Bao, 2016) BLOND (Kriechbaumer & Jacobsen, 2018) a In the original version (2012) there is data for three houses between 3 and 4 weeks, but only one of them contains submetered data. In the 2017 edition there is data for seven houses during 3 years.…”