A shape-based clustering method for pattern recognition of residential electricity consumption

Wen, Lulu; Zhou, Kaile; Yang, Shanlin

doi:10.1016/j.jclepro.2018.12.067

Cited by 95 publications

(26 citation statements)

References 42 publications

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“…Electricity demand from a residence is something which could be shaped up to a point, and it is time for people to start getting discipline and education about modifying their electricity consumption patterns (7) , because nobody knows when energy will be on scarcity, or people should depend on rules for its usage, among others. In any case, a good tool to empower people about the energy administration is to involve them in the whole process of modernization of the grid (8) .…”

Section: Electricity Demand From Residentialsmentioning

confidence: 99%

Optimization of the implementation design of distributed energy resources in micro-grids

Robalino

Andrade

2021

inycomp

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This article develops a study about the energy resources available in microgrids, extracting and analyzing data form satellite databases, in order to apply it to the technologies available. On the other hand, a demand estimation is carried out based on measurements, and finally an optimization is performed to determine the most economically profitable option with the correspondent graphs and analysis.

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Section: Electricity Demand From Residentialsmentioning

confidence: 99%

Optimization of the implementation design of distributed energy resources in micro-grids

Robalino

Andrade

2021

inycomp

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“…In traditional pattern recognition methods, the most important thing is to express this image through a mathematical statistical model after extracting a certain amount of artificial feature points [5][6] . Then identify the image by the method of image matching.…”

Section: Introductionmentioning

confidence: 99%

“…The core of the high-speed network is the addition of two nonlinear conversion layers to the ordinary neural network. One is T (transform gate) and one is C (carry gate), as shown in equation (5).…”

Section: Resnet Introductionmentioning

confidence: 99%

Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm

Tian

2020

IEEE Access

149

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As an algorithm with excellent performance, convolutional neural network has been widely used in the field of image processing and achieved good results by relying on its own local receptive fields, weight sharing, pooling, and sparse connections. In order to improve the convergence speed and recognition accuracy of the convolutional neural network algorithm, this paper proposes a new convolutional neural network algorithm. First, a recurrent neural network is introduced into the convolutional neural network, and the deep features of the image are learned in parallel using the convolutional neural network and the recurrent neural network. Secondly, according to the idea of ResNet's skip convolution layer, a new residual module ShortCut3-ResNet is constructed. Then, a dual optimization model is established to realize the integrated optimization of the convolution and full connection process. Finally, the effects of various parameters of the convolutional neural network on the network performance are analyzed through simulation experiments, and the optimal network parameters of the convolutional neural network are finally set. Experimental results show that the convolutional neural network algorithm proposed in this paper can learn the diverse features of the image, and improve the accuracy of feature extraction and image recognition ability of the convolutional neural network. INDEX TERMS Convolutional neural network; Artificial intelligence; Image recognition.

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“…Rasanen et al [17] partitioned customers into electricity user groups based on similar electricity usage behavior with the SOM, k-means, and hierarchical clustering algorithms. Similarly, to group electricity consumption profiles, Wen et al [18] investigated a shape-based clustering method.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data

Ullah

Haydarov

Haq

et al. 2020

Sensors

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The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clustering-based analysis of energy consumption to categorize the consumers’ electricity usage into different levels. First, a deep autoencoder that transfers the low-dimensional energy consumption data to high-level representations was trained. Second, the high-level representations were fed into an adaptive self-organizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis.

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A shape-based clustering method for pattern recognition of residential electricity consumption

Cited by 95 publications

References 42 publications

Optimization of the implementation design of distributed energy resources in micro-grids

Optimization of the implementation design of distributed energy resources in micro-grids

Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm

Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data

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