Uruguay Electricity Consumption: Montevideo: Large Consumer data is updated yearly, averaging 850.484 GWh (Median) from Dec 1995 to 2016, with 22 observations. The data reached an all-time high of 1,056.477 GWh in 2014 and a record low of 412.000 GWh in 1995. Contact online >>
Uruguay Electricity Consumption: Montevideo: Large Consumer data is updated yearly, averaging 850.484 GWh (Median) from Dec 1995 to 2016, with 22 observations. The data reached an all-time high of 1,056.477 GWh in 2014 and a record low of 412.000 GWh in 1995.
This article introduces a dataset containing electricity consumption records of residential households in Uruguay (mostly in Montevideo). The dataset is conceived to analyze customer behavior and...
This article introduces a dataset containing electricity consumption records of residential households in Uruguay (mostly in Montevideo). The dataset is conceived to analyze customer behavior and detect patterns of energy consumption that can help to improve the service.
Uruguay Electricity Consumption: Montevideo: Residential data is updated yearly, averaging 1,221.000 GWh (Median) from Dec 1995 to 2016, with 22 observations. The data reached an all-time high of 1,414.000 GWh in 2000 and a record low of 1,176.312 GWh in 2006.
Uruguay Electricity Consumption: Montevideo: Residential data is updated
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Received 2021 Jul 19; Accepted 2021 Dec 9; Collection date 2022.
The Creative Commons Public Domain Dedication waiver applies to the metadata files associated with this article.
Subject terms: Energy supply and demand, Energy modelling, Energy modelling
Schematic overview of the designed system for collecting and processing household electric consumption data.
Summary of the four subsets contained in ECD-UY.
*Periods may vary depending on the customer.
Smart meter Kaifa, model MA110P, installed by the Uruguayan electricity company, UTE (image by UTE, https://portal.ute .uy/medicion-inteligente).
Devices installed by the Uruguayan electricity company, UTE (images by UTE).
Dwellings characteristics of the subset of disaggregated electricity consumption by appliances.
Architecture and processes of the communication system for the aggregate energy consumption dataset.
Architecture and processes of the communication system for the electric water heater consumption dataset.
Description of the records corresponding to the information of customers, present in the file customers.csv.
Description of records in files of the total household consumption dataset.
Description of records in files of the electric water heater dataset.
Description of records in the disaggregated energy consumption by appliance dataset.
Number of days per interval of number of records.
Number of days by customer, for all days and complete days (at least 95% of energy consumption records).
Histogram of customers with recorded consumption (all days and complete days, i.e., at least 95% of records).
Mean energy consumption of one day for four customers (customer #8037 from 01/01/2019 to 03/11/2020, customer #97875 from 21/03/2019 to 07/11/2020, customer #109846 from 01/01/2019 to 03/11/2020, and customer #110088 from 01/01/2019 to 02/11/2020).
Mean energy consumption for a day.
Example of one month of total household consumption with outliers and missing values, and its subsequent correction, for customer #69806.
24-hours sample of electric water heater consumption for customer #115609 and two possible corrections for outlier values: set outliers to zero (red circles), and set outliers to the nominal power value of the water heater (green circles).
Mean total and electric water heater power consumption for a day, using the 135 households that counts with both kinds of consumption. Used period: from 15/07/2019 to 30/11/2019.
Example of electric water heater consumption patterns.
Detailed information about the recording duration and data gaps.
Mean power demanded by hour (histograms) and in a day (stack bar), for each appliance present in the subset.
This research was partly supported by CSIC-UDELAR and UTE through the project "Computational intelligence for characterization of electric energy consumption in residential households". The work of S. Nesmachnow is partly supported by ANII and PEDECIBA, Uruguay. The publication fee is partly supported by PEDECIBA and CSIC-UDELAR, Uruguay, and personal funds. We thank Liliana Corna and Gastón Hernández (UTE) for helpful comments and corrections.
Juan Chavat: Data analysis, data cleansing, code development, manuscript writing, manuscript revision and correction. Sergio Nesmachnow: Data analysis, manuscript writing, manuscript revision and correction. Jorge Graneri: Data analysis. Gustavo Álvez: Data analysis, manuscript writing.
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