Abstract
Most cluster identification studies regarding consumer electricity load is faced with problems of erroneous clustering method similarity, low clustering quality and poor identification accuracy. To solve these problems, this paper utilizes the elbow method, k- Means ++, entropy weight method and a graph convolutional neural network to provide a means for cluster identification based on electrical appliance power data collected via smart sockets. In this article, elbow and entropy weight methods were used to achieve the adaptive clustering algorithm. To obtain the electrical appliance load curves, Euclidean and dynamic time warping (DTW) distances were integrated and the similarity measurement method was used to improve the k-means ++ algorithm, which was then applied to data collected via smart socket clustering. Next, clustering results were input into the graph convolutional neural network (GCN) for identification purposes and appliance type information was obtained. Finally, experiments were conducted using electrical load data from 20 commercial users. The method used was a combination of the k-means algorithm and long short-term memory network (LSTM). The results show that under optimal K value conditions (as determined by the elbow score), the methods used in this paper have improved clustering quality and recognition accuracy, when compared to LSTM.
Highlights
In recent years, with the proposal of smart cities and the continuous development of smart grid technology, the importance of smart power usage has increased
To improve the poor quality of clustering results, low recognition accuracy, and intelligence to be improved in the prior art, this paper proposes a power load identification method based on an improved graph convolutional neural network (GCN)
The Long short-term memory (LSTM) and Bi-directional Long Short-Term Memory (Bi-LSTM) in the Recurrent Neural Network (RNN) commonly used in the load identification field will be used to contrast with GCN
Summary
With the proposal of smart cities and the continuous development of smart grid technology, the importance of smart power usage has increased. The Euclidean distance algorithm is currently the mainstream measure of similarity; it only considers the value distribution between the time points of the daily load curve, which leads to its poor performance in reflecting the dynamic characteristics of the slope and shape of the curve. Because the extraction object is user electrical appliance load data, it is not sufficient to use the Euclidean distance for clustering. To solve this problem, the combination of the DTW distance and Euclidean distance with the entropy weight method is used to increase the clustering accuracy
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
