A generalized economic model for optimally selecting forecasted load profiles for measuring demand response in residential energy management system

Jean-Paul, Peter; Lie, Tek Tjing; Anderson, Timothy; Vallès, Brice

doi:10.1002/er.6857

Cited by 1 publication

(1 citation statement)

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“…Residential load monitoring and providing real and direct feedback on the amount of electrical equipment consumption in buildings to consumers can have high potential in various beneficial applications such as awareness of energy consumption and conservation, controllable load quantitative assessment, providing accurate planning of energy consumption, and, finally, leading to minimizing the mismanagement of energy consumption [10,11]. This can also facilitate interactions between energy users and producers through load management programs, so that energy producers will be able to formulate energy saving policies for the use of individual appliances and consumers, based on the feedback from the producers, will be consciously able to do saving options [12,13]. A customer's awareness of the mean consumption of each of their household electrical appliances (HEAs) enables them to save on the use of inefficient appliances and be able to control their consumption at peak hours to avoid penalties.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Monitoring of Residential Loads via Laplacian Eigenmaps and Hybrid Deep Learning Procedures

et al. 2022

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Today, introducing useful and practical solutions to residential load disaggregation as subsets of energy management has created numerous challenges. In this study, an intelligence hybrid solution based on manifold learning and deep learning applications is presented. The proposed solution presents a combined structure of Laplacian eigenmaps (LE), a convolutional neural network (CNN), and a recurrent neural network (RNN), called LE-CRNN. In the proposed model architecture, LE, with its high ability in dimensional reduction, transfers the salient features and specific values of power consumption curves (PCCs) of household electrical appliances (HEAs) to a low-dimensional space. Then, the combined model of CRNN significantly improves the structure of CNN in fully connected layers so that the process of identification and separation of the HEA type can be performed without overfitting problems and with very high accuracy. In order to implement the suggested model, two real-world databases have been used. In a separate scenario, a conventional CNN is applied to the data for comparing the performance of the suggested model with the CNN. The designed networks are trained and validated using the PCCs of HEAs. Then, the whole energy consumption of the building obtained from the smart meter is used for load disaggregation. The trained networks, which contain features extracted from PCCs of HEAs, prove that they can disaggregate the total power consumption for houses intended for the Reference Energy Disaggregation Data Set (REDD) and Almanac of Minutely Power Dataset (AMPds) with average accuracies (Acc) of 97.59% and 97.03%, respectively. Finally, in order to show the accuracy of the developed hybrid model, the obtained results in this study are compared with the results of similar works for the same datasets.

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