Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014

Abstract

With the cost of consuming resources increasing (both economically and ecologically), homeowners need to find ways to curb consumption. The Almanac of Minutely Power dataset Version 2 (AMPds2) has been released to help computational sustainability researchers, power and energy engineers, building scientists and technologists, utility companies, and eco-feedback researchers test their models, systems, algorithms, or prototypes on real house data. In the vast majority of cases, real-world datasets lead to more accurate models and algorithms. AMPds2 is the first dataset to capture all three main types of consumption (electricity, water, and natural gas) over a long period of time (2 years) and provide 11 measurement characteristics for electricity. No other such datasets from Canada exist. Each meter has 730 days of captured data. We also include environmental and utility billing data for cost analysis. AMPds2 data has been pre-cleaned to provide for consistent and comparable accuracy results amongst different researchers and machine learning algorithms.

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

“…This was already accomplished in datasets like AMPds (Makonin et al, 2016) and RAE . However, since many loads can be attached to the same circuit (e.g., the kitchen circuit contains many different appliances), this solution alone will not guarantee that individual consumption is available for every load.…”

“…Tracebase (Reinhardt et al, 2012) Dataport (Holcomb, 2012) AMPds (Makonin, Ellert, Bajić, & Popowich, 2016) iAWE (Batra, Gulati, Singh, & Srivastava, 2013) IHEPCDS (Bache & Lichman, 2013) ACS-Fx (Gisler, Ridi, Zufferey, Khaled, & Hennebert, 2013;Ridi, Gisler, & Hennebert, 2014) UK-DALE (Kelly & Knottenbelt, 2015) REFIT (Murray et al, 2015) GREEND (Monacchi, Egarter, Elmenreich, D'Alessandro, & Tonello, 2014) PLAID I and II (Baets et al, 2017;Gao, Giri, Kara, & Bergés, 2014) RBSA (Ecotope Inc, 2014) COMBED DRED (Uttama Nambi, Reyes Lua, & Prasad, 2015) HFED (Gulati, Ram, & Singh, 2014) and energy estimation (EE) algorithms. One possibility is using a resampling technique (e.g., bootstrapping or jackknifing) to generate new labels from the existing ones, that can later be used as training data for event classification.…”

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

“…The first real dataset has over 400 million raw energy consumption records from five houses having a time resolution of 6 s. It was reduced to just over 20 million during pre-processing phase without loss of accuracy or precision. Similarly, the second real dataset AMPds2 [34] was reduced to 4 million records from over 21 million raw records, which initially had 1-min time-resolution. Additionally, we constructed a synthetic dataset for preliminary evaluation of our model, having over 1.2 million records.…”

“…For the evaluation of the proposed model, we used three datasets of energy time series: the UK Domestic Appliance Level Electricity dataset (UK-Dale) [33]-time series data of power consumption collected from 2012 to 2015 with time resolution of six seconds for five houses with 109 appliances from Southern England, and the AMPds2 [34] dataset-time series data of power consumption collected from a residential house in Canada from 2012 to 2014 at a time resolution of one minute, and a synthetic dataset. A more detailed explanation on datasets is presented in Section 3.2 with a sample of raw data.…”

Show me the data

“…For data in repositories, inclusion of descriptive or supporting information offering essential information on the experimental or computational processes, the survey structure and methodology, the curation approach, and so on, is vital. Data journals like Scientific Data are a welcome and useful venue, in that regard, and are already providing valuable insights into energy-relevant domestic consumption datasets 6 ; more tools that connect such documentation with repositories will help with the growth of data sharing practices.…”