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Home electricity demand has increased uninterrupted and is expected in 2050 to doubles the demanded in 2010. Making reasonable use of electricity is increasingly important and, in that way, different policies are carried out based on knowledge of how it is used. This article presents a procedure for measuring the potential electricity consumption i...
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... ownership of certain household appliances. Based on these appliances and using the information of local shops with presence on the Internet, power consumption data was collected. Up to five di↵erent appliance models were gathered to define the median power consumption of each appliance. Table 2 lists the result of the data collection process, and Fig. 1 presents a bar graph of the mean power consumption together with its standard deviation measure. It can be observed how some appliances are more energyintensive than others. ...
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Worldwide, residential electricity demand has increased constantly, expecting to double in 2050 the demand of 2010. Different policies have been proposed to achieve a smart use of electricity. This article presents a data-analysis approach to evaluate the potential household electricity consumption from statistical data. The main axis of the study...
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... Beyond the main differences, it should be noted that Chavat and Nesmachnow's works 23,24 used variables for the construction of their model that can be catalogued as relevant ones. These variables were: information about appliance ownership, georeferentiation, and the number of rooms per dwelling. ...
Given the massive deployment of smart meters at international level, it is necessary to develop methodologies to extract knowledge from the data that they can provide. To this end, it is necessary to associate energy, socio-demographic and/or technical-constructive data, because this is the only way to identify profiles with their corresponding relevant variables or drivers. The usual problem is that socio-technical information about users is limited or non-existent, as it is costly to collect. Consequently, this work presents as a novelty the use of census information to characterize groups of urban segments with similar daily electricity load curves, which avoids the need to collect socio-technical information through specific surveys or direct measurements. In this way, relevant variables are identified in the determination of consumption patterns in the study case (Montevideo-Uruguay) and they are used to infer the daily behavior of those sectors of the city that don’t have this information.
... In turn, data gathered in ECD-UY is also very valuable for the electricity company, in order to study and analyze electricity consumption patterns of citizens, relating the consumption with relevant socio-demographic data and indicators 27,28 , the design of personalized electricity billing plans for different segments of the population, and the study of specific interventions to influence on the users' behavior to achieve a rational utilization of www.nature.com/scientificdata www.nature.com/scientificdata/ the electric resources, among others relevant issues related to the intelligent utilization of electricity in modern smart cities. ...
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. The dataset is conformed by three subsets that cover total household consumption, electric water heater consumption, and by-appliance electricity consumption, with sample intervals from one to fifteen minutes. The datetime ranges of the recorded consumptions vary depending on the subset, from some weeks long to some years long. The data was collected by the Uruguayan electricity company (UTE) and studied by Universidad de la República. The presented dataset is a valuable input for researchers in the study of energy consumption patterns, energy disaggregation, the design of energy billing plans, among other relevant issues related to the intelligent utilization of energy in modern smart cities.
This article presents a study to characterize the electricity consumption in residential buildings in Uruguay. Understanding residential electricity consumption is a relevant concept to identify factors that influence electricity usage, and allows developing specific and custom energy efficiency policies. The study focuses on two home appliances: air conditioner and water heater, which represents a large share of the electricity consumption of Uruguayan households. A data-analysis approach is applied to process several data sources and compute relevant indicators. Statistical methods are applied to study the relationships between different relevant variables, including appliance ownership, average income of households, and temperature, and the residential electricity consumption. A specific application of the data analysis is presented: a regression model to determine the consumption patterns of water heaters in households. Results show that the proposed approach is able to compute good values for precision, recall and F1-score and an excellent value for accuracy (0.92). These results are very promising for conducting an economic analysis that takes into account the investment cost of remotely controlling water heaters and the benefits derived from managing their demand.
