Energy and flow demand analysis of domestic hot water in an apartment complex using a smart meter

Lee, Jae Yong; Yim, Taesu

doi:10.1016/j.energy.2021.120678

Cited by 22 publications

(11 citation statements)

References 27 publications

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“…The majority of the information in the database comes from smart water and energy meters [24], [25], [26]. Smart meters are able to collect and transmit consumption data to a central location, allowing for real-time monitoring of usage.…”

Section: Water and Electricity Consumption Data Collectionmentioning

confidence: 99%

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Bashir

2023

IEEE Access

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This work studies the automatic classification of water consumption patterns and electrical devices, both supervised and unsupervised. This involves training machine learning algorithms to identify normal and abnormal water consumption patterns and differentiate between different types of electrical devices. We performed an unsupervised classification of consumer water patterns in direct and indirect ways. The first is to use the raw consumption patterns obtained directly from the server. The second one corresponds to the use of the sampled consumption patterns. This classification is performed using hierarchical bottom-up classification and a self-organizing map. A probabilistic analysis of daily water consumption is performed to extract the percentage of daily consumption with the most information. It enables us to identify water consumption patterns more quickly by reducing the number of data points for each daily pattern, allowing us to recognize and classify anomalous behaviors as soon as feasible. Then, the signatures of electrical devices are classified using three ML algorithms: multilayer perceptron (MLP), k nearest neighbor (KNN), and decision tree (DT). Furthermore, assembly approaches are also studied. These are based on the OAA (One Against All) principle, which presents one class against all other classes, and the ECOC (Error-Correcting Output Codes) philosophy, which allows the classification error to be corrected. According to the bias/variance trade-off, both techniques enhanced classification by ensuring accuracy and generalization. A more in-depth analysis of the properties of the electrical devices is handled with an esemplastic approach based on gradient-boosting decision trees. The features of electrical devices can be extracted using this analysis based on the nature of their harmonic signatures.

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Section: Water and Electricity Consumption Data Collectionmentioning

confidence: 99%

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Bashir

2023

IEEE Access

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“…For this purpose, probabilistic methods or neural networks are used, as indicated by the authors of the studies presented in the works, respectively, [46,47]. Due to the important role of the method of use for temporary water abstraction, the authors of the studies [48] write clearly not about its calculation, but "prediction", emphasizing the stochastic nature of this consumption, depending on the way the system is used by users. The authors of the review [54] also emphasize a significant impact on energy consumption in buildings, which, although it concerns heating, ventilation and lighting systems, seems to indicate a similar unpredictability of their use, as in the case of DHW systems.…”

Section: Hot Water Consumption-standard Methodsmentioning

confidence: 99%

“…Probabilistic model for predicting occupancy and domestic hot water use was also presented in [47]. In the paper [48], the smart-meter was used to measure the instantaneous consumption of hot water, and the results of the analysis provided in the paper showed that "statistical and machine learning analysis can forecast the seasonal DHW demand". Nevertheless, the tools used in the above-mentioned works go beyond the workshop of typical engineers, as well as scientists who deal with the subject of improving the energy efficiency of DHW systems by improving control systems or changing the design of system components.…”

Section: Introductionmentioning

confidence: 99%

Peak Power of Heat Source for Domestic Hot Water Preparation (DHW) for Residential Estate in Poland as a Representative Case Study for the Climate of Central Europe

Amanowicz

2021

Energies

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Due to the energy transformation in buildings, the proportions of energy consumption for heating, ventilation and domestic hot water preparation (DHW) have changed. The latter component can now play a significant role, not only in the context of the annual heat demand, but also in the context of selecting the peak power of the heat source. In this paper, the comparison of chosen methods for its calculation is presented. The results show that for contemporary residential buildings, the peak power for DHW preparation can achieve the same or higher value as the peak power for heating and ventilation. For this reason, nowadays the correct selection of the peak power of a heat source for DHW purposes becomes more important, especially if it uses renewable energy sources, because it affects its size and so the investment cost and economic efficiency. It is also indicated that in modern buildings, mainly accumulative systems with hot water storage tanks should be taken into account because they are less sensitive to design errors (wrongly selected peak value in the context of the uncertainty of hot water consumption) and because they result in acceptable value of peak power for DHW in comparison to heating and ventilation.

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“…Submetering for electric vehicle charging has not been receiving much attention other than some EV pilot projects in the US and the recent CPUC's decision which allows submetering in California. The focus has been mostly to use submetering measurements in demand-side response energy-saving problems for smart appliances such as dishwashes, washing machines and hot water buffers (Vanthournout et al, 2015;Yin et al, 2016;Jae Yong and Yim, 2021). We provide complementary information to the Californian pilot by also considering the network tariff impact of a larger scale adoption using submetering solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Unlocking Flexible Electric Vehicle Charging via New Rate Design

Gomes¹,

Abdin²,

Puchinger³

et al. 2024

The Energy Journal

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Energy and flow demand analysis of domestic hot water in an apartment complex using a smart meter

Cited by 22 publications

References 27 publications

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Peak Power of Heat Source for Domestic Hot Water Preparation (DHW) for Residential Estate in Poland as a Representative Case Study for the Climate of Central Europe

Unlocking Flexible Electric Vehicle Charging via New Rate Design

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