Adaptive model predictive control for electricity management in the household sector

Colmenar‐Santos, Antonio; Muñoz-Gómez, Antonio-Miguel; Rosales-Asensio, Enrique; Aznar, Gregorio  Fernandez; Galan-Hernandez, Noemi

doi:10.1016/j.ijepes.2021.107831

Cited by 11 publications

(4 citation statements)

References 35 publications

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“…• Normal and Noisy datasets: For both datasets, only four features out of the 16 initiated features are chosen (1,2,4,6). On both datasets, the classification accuracy was 100% and the algorithm took less than one second to classify all devices.…”

Section: E Feature Reduction Of Electrical Devices Signaturesmentioning

confidence: 99%

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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: E Feature Reduction Of Electrical Devices Signaturesmentioning

confidence: 99%

“…New environmental challenges are emerging in the area of energy and water resource management [1], [2]. To overcome these issues, novel advances must be explored, particularly in the everyday usage and processing of this limited quantity of resources.…”

Section: Introductionmentioning

confidence: 99%

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

Bashir

2023

IEEE Access

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“…As water scarcity continues to be a pressing global issue, initiatives like Kumar's underscore the importance of technological innovation in addressing sustainability challenges and ensuring efficient resource allocation. The development of a mobile app for smart electricity usage monitoring by Anagha Choudhari and team marks a significant step forward in empowering users to track and optimize their energy consumption patterns [2]. This app, likely built on user-friendly interfaces and data visualization techniques, provides real-time insights into electricity usage, enabling users to identify energy-intensive activities and implement strategies for conservation.…”

Section: Literature Surveymentioning

confidence: 99%

Hydro Electrometric Tracking Application

2024

Data Analytics and Artificial Intelligence

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We offer a brand-new technique for real-time control over the project's water flow rate and electricity usage. Unlike current systems, which are frequently costly and solely monitor power or water, our method integrates both parameters into a single, reasonably priced platform. Our solution allows users to track their resource consumption in real-time by utilizing Arduino-based sensors and a user-friendly mobile application created with MIT App Inventor. By providing consumers with the knowledge, they need to make educated decisions about how much water and power to use, this promotes more effective and sustainable resource management. It is unique in that it provides an affordable solution that integrates water and power metering capabilities, solving major issues with current systems.

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“…An accurate prediction of the consumption at each hour of a day can be used to make decisions to try to reduce them as much as possible. In [14], an adaptive predictive control algorithm, using mixed linear programming, is proposed. Moreover, in [15], a twostage control algorithm for the centralized management of residential loads is proposed to ensure their control.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Household Appliances Power Consumption

et al. 2022

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The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict household electricity consumption. As a novelty, this paper proposed a method of predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov–Smirnov Test and Pearson’s X2 test). To test the veracity of the evaluations, first, a set of random values was simulated for each hour, and their respective averages were calculated. These were compared with the averages of the real values for each hour. With the exception of HVAC during working days, great results were obtained. For the refrigerator, the maximum error was 3.91%, while for the lighting, it was 4.27%. At the point of consumption, the accuracy was even higher, with an error of 1.17% for the dryer while for the washing machine and dishwasher, their minimum errors were less than 1%. The error results confirm that the applied methodology is perfectly acceptable for modeling household appliance consumption and consequently predicting it. However, these consumptions can be only extrapolated to dwellings with similar surface areas and habitats.

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Adaptive model predictive control for electricity management in the household sector

Cited by 11 publications

References 35 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

Hydro Electrometric Tracking Application

Modeling and Simulation of Household Appliances Power Consumption

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