Charging Load Pattern Extraction for Residential Electric Vehicles: A Training-Free Nonintrusive Method

Xiang, Yue; Wang, Yang; Xia, Shiwei; Teng, Fei

doi:10.1109/tii.2021.3060450

Cited by 20 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xiang et al invented a Harris detection operator theory, the core idea of which is based on the signal autocorrelation function; by extracting features such as small-angle rotations within image elements, a sub-pixel level of accuracy is finally achieved. The advantages of this method are reflected in the processing of image edge noise and the improvement of detection efficiency; therefore, its robustness is excellent [ 3 ]. Harahap et al proposed the SIFT algorithm, a descriptive algorithm based on local point features in the scale space, and summarized and refined it.…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

Wang

2022

International Journal of Analytical Chemistry

View full text Add to dashboard Cite

In order to solve the research on the extraction of ceramic sculpture artwork patterns, and, in the process of image reproduction, the problem of too few feature points in the object image, the author proposes an image stitching algorithm that combines SIFT and MSER algorithms. After comprehensively analyzing the principles, advantages, and disadvantages of the current main image stitching methods, in terms of feature matching, based on the K-D tree search algorithm, the improved BBF algorithm is used to improve the search efficiency of feature points. In order to remove the possible cracks in the stitching process, an improved multiband fusion algorithm is used to seamlessly stitch the registered images. The results show that the feature points detected by the one-dimensional normal distribution algorithm are on average 0.1%, 0.5%, 1.7%, 4.4%, and 9.2%. The algorithm combining SIFT and MSER to extract feature points can reach 3.6%, 4.6%, 8.4%, 15%, and 19.1%. The experimental results show that the algorithm proposed by the author can extract more image feature points to facilitate later image registration. The image blur phenomenon in the original image fusion algorithm is solved, and a complete and clear two-dimensional plane pattern is finally obtained.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

Wang

2022

International Journal of Analytical Chemistry

View full text Add to dashboard Cite

show abstract

“…The generative adversarial network (GAN) have powerful modeling capabilities [25] . The core of GAN is that the generator (G) and the discriminator (D) realize the learning optimization through the zero-sum game, and finally reach the Nash equilibrium.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Non-intrusive modeling for integrated energy system based on two-stage GAN

Sun

Ren

et al. 2022

iEnergy

View full text Add to dashboard Cite

Generally, an accurate model can describe the operating states of a system more effectively and provide a more reliable theoretical basis for the system optimization and control. Different from the traditional intrusive modeling, a non-intrusive modeling method based on two-stage generative adversarial network (TS-GAN) is proposed for integrated energy system (IES). By using this method, non-intrusive modeling for the IES including photovoltaic, wind power, energy storage, and energy coupling equipment can be carried out. First, the characteristics of IES are analyzed and extracted based on the meteorological data, energy output, and energy price, and then the characteristic database is established. Meanwhile, the loads are classified as uncontrollable loads and schedulable loads based on frequency domain decomposition to facilitate energy management. Furthermore, TS-GAN algorithm based on the Stackelberg game is designed. In the TS-GAN, the first-stage GAN is used to generate the operating data of each equipment identified by non-invasive monitoring, and the second-stage GAN distinguishes the accumulated data generated by firststage GAN and further modifies the generator models of the first-stage GAN. Finally, the effectiveness and accuracy of the proposed method are verified by the simulation of an energy region.

show abstract

“…It is accepted as a foresight to the uncertainty problem of the EV charging power during peak hours of the grid. In particular, the specified foresight is required to supply the EV charging load along with other basic loads in the existing electrical infrastructure in a house [26]. In a paper following this foresight [27], they improved EV charging demand error performance in the parking lots by estimating conventional load and EV load separately.…”

Section: Introductionmentioning

confidence: 99%

Smart coordination of predictive load balancing for residential electric vehicles based on EMD‐Bayesian optimised LSTM

Akil

Dokur

Bayındır

2022

IET Renewable Power Gen

View full text Add to dashboard Cite

The charging load forecasting of residential Electric Vehicles help grid operators make informed decisions in terms of scheduling and managing demand response. The residence can include integrated residential appliances with multi-state and high-frequency features. For this reason, it is difficult to estimate the total load of residence accurately. To overcome this problem, this paper proposes a hybrid forecasting model using the empirical mode decomposition and Bayesian optimised Long Short-Term Memory for load balancing based on residential electricity meter data. The residential electricity meter data includes three datasets as Electric Vehicle, heat pump and photovoltaic system. To decompose of the data characteristics, the empirical mode decomposition method performs to the original data. Then, the Bayesian optimised Long Short-Term Memory is applied to forecast for each sub-component of the data sequentially. The main features of the proposed model include a significant improvement in prediction accuracy and capture the local maximums. The advantage of the proposed method over existing methods are also verified over with experiments of data-driven on the IEEE 33 busbar test system. The result of simulation forecasting model indicates that predict closely the busbar outflow power, voltage drop, transformer loading states and power losses to compare with actual load model.

show abstract

Charging Load Pattern Extraction for Residential Electric Vehicles: A Training-Free Nonintrusive Method

Cited by 20 publications

References 27 publications

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

Non-intrusive modeling for integrated energy system based on two-stage GAN

Smart coordination of predictive load balancing for residential electric vehicles based on EMD‐Bayesian optimised LSTM

Contact Info

Product

Resources

About