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A day (14 November) of the electricity consumption of an electric water heater (customer id. 115747 from dataset ECD-UY) after refilling the data gaps.
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The methodology described in this article is applicable to design proper management strategies for demand response in smart electricity grids to fairly select water heaters to intervene while guaranteeing the lower discomfort of users.
Abstract
Demand-response techniques are crucial for providing a proper quality of service un...
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With the rapid increase in electric vehicle (EV) ownership, the uncertainty of EV charging demand has become a significant concern, especially in distributed photovoltaic (PV) power distribution networks (DNs) with high penetration rates. This growing demand presents challenges in meeting the needs of EV owners and grid charging/discharging station...
Citations
... Previous studies into smart controls for EWHs show that two concerns should be kept in mind when implementing water heater controls for a community. The first concern is also the bottom line for field deployment-the domestic hot water temperature should never be too low [14]. Secondly, the limited access to real-time data-due to either hardware limit or privacy concerns-is a significant obstacle [15]. ...
As the smart grid involves more new technologies such as electric vehicles (EVs) and distributed energy resources (DERs), more attention is needed in research to general energy storage (GES) based energy management systems (EMS) that account for all possible load shifting and control strategies, specifically with major appliances that are projected to continue electrification such as the electric water heater (EWH). In this work, a methodology for a modified single-node model of a resistive EWH is proposed with improved internal tank temperature for user comfort modeling and capabilities for conservation voltage reduction (CVR) simulations as well as Energy Star and Consumer Technology Association communications protocol (CTA-2045) compliant controls, including energy storage calculations for “energy take”. Daily and weekly simulations are performed on a representative IEEE test feeder distribution system with experimental load and hot water draw (HWD) profiles to consider user comfort. Sequential controls are developed to reduce power spikes from controls and lead to peak shavings. It is found that EWHs are suitable for virtual power plant (VPP) operation with sustainable tank temperatures, i.e., average water temperature is maintained at set-point or above at the end of the control period while shifting up to 78% of EWH energy out of shed windows per day and 75% over a week, which amounts to up to 23% of the total load shifted on the example power system. While CVR simulations reduced the peak power of individual EWHs, the aggregation effect at the distribution level negates this reduction in power for the community. The EWH is shown as an energy constant load without consistent benefit from CVR across the example community with low energy reductions of less than 0.1% and, in some cases, increased daily energy by 0.18%.
... Porteiro et al. [21] Thermal Discomfort Index ...
Electric Storage Water Heaters (ESWH) are a widespread solution to supply domestic hot water (DHW) to dwellings and other applications. The working principle of these units makes them a great resource for peak shaving, which is particularly important due to the level of penetration renewable energies are achieving and their intermittent nature. Renewable energy deployment in the electricity market translates into large electricity price fluctuations throughout the day for individual users. The purpose of this study was to find a demand–response strategy for the activation of the heating element based on a multiobjective minimization of electricity cost and user discomfort, assuming a known DHW consumption profile. An experimentally validated numerical model was used to perform an evaluation of the potential savings with the demand–response optimized strategy compared to a thermostat-based approach. Results showed that cost savings of approximately 12% can be achieved on a yearly basis, while even improving user thermal comfort. Moreover, increasing the ESWH volume would allow (i) more aggressive demand–response strategies in terms of cost savings, and (ii) higher level of uncertainty in the DHW consumption profile, without detriment to discomfort.
... Effective energy management strategies must be complemented with easy-tounderstand and easy-to-use computer-assisted applications, to properly involve citizens and organizations, and encourage then to be part of the improved energy utilization model. On the one hand, electricity companies should be able to implement effective demand response actions, properly evaluated in advance to reduce the negative impacts on users comfort [21]. On the other hand, citizens should have available useful applications for monitoring, managing, and evaluating the energy consumption at household level [15]. ...
This article presents an approach for energy-aware smart home planning via direct control and planning of residential electric appliances for smart cities. Energy-aware planning is a crucial concept for achieving a better utilization of resources and improving the quality of life in modern cities. In this line of work, this article proposes a stochastic optimization approach to plan the utilization of domestic appliances considering stochastic user preferences. A specific case study is addressed, considering residential households in Montevideo, Uruguay. The case study is modeled using built using real data from existing appliances and a data analysis approach for modeling user preferences. The proposed approach is able to compute accurate plannings in the experimental evaluation performed. The developed approach contributes to energy efficiency and sustainability in modern smart cities.
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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.
This article presents an approach applying computational intelligence for detecting the use of air conditioners in households. The main objective is determining the intensive use of air conditioning with a high level of confidence. Unsupervised (-means) and supervised (Artificial Neural Networks) approaches are developed for classifying consumers for a case study in Uruguay, using data collected by a smart meters network and open weather data. Data from 29 Uruguayan cities were considered in the period from January 1, 2022, to December 31, 2022. Two thermal models are developed for estimating the temperature inside households. The main results indicate that the proposed approach is able to reach a high classification accuracy, up to 94.5% and a high classification recall, up to 95%, for the considered real case study. The final scope of the work is developing smart tools for classifying consumers, to design and suggest specific commercial products that promote energy efficiency.
This article presents an unsupervised machine learning approach for the problem of detecting use of air conditioning in households, during the summer. This is a relevant problem in the context of the modern smart grid approach under the paradigm of smart cities. The proposed methodology applies data analysis, a thermal inertial model for estimating the temperature inside a household, statistical analysis, clustering, and classification. The proposed model is validated on a real case study, considering households with known use of air conditioning in summer. In the evaluation, the proposed classification methodology reached an accuracy of 0.897, a promising result considering the very small cardinality of the set of households. The proposed method is valuable since it applies an unsupervised approach, which does not require large volumes of labeled data for training, and allows determining characteristics in the electricity consumption patterns that are useful for categorization. In turn, it is a non-intrusive method and does not require investing in the installation of complex devices or conducting consumer surveys.KeywordsUnsupervised learningData analysisResidential electricity consumption
This article presents an approach for energy-aware smart home planning via direct control and planning of residential electric appliances for smart cities. Energy-aware planning is a crucial concept for achieving a better utilization of resources and improving the quality of life in modern cities. In this line of work, this article proposes a stochastic optimization approach to plan the utilization of domestic appliances considering stochastic user preferences. A specific case study is addressed, considering residential households in Montevideo, Uruguay. The case study is modeled using built using real data from existing appliances and a data analysis approach for modeling user preferences. The proposed approach is able to compute accurate plannings in the experimental evaluation performed. The developed approach contributes to energy efficiency and sustainability 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.
