Electricity consumption behaviour analysis based on adaptive weighted‐feature K‐means‐AP clustering

Li, Chunyan; Cai, Wenyue; Yu, Chang-Qing; Zhao, Rongsheng; Zhang, Qian

doi:10.1049/iet-gtd.2018.5286

Cited by 15 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Time domain along with fluctuation characteristics was considered in [65] in the local modeling, where adaptive Kmeans was used to cluster the load curves.…”

Section: ) Adaptive K-meansmentioning

confidence: 99%

Electric Load Clustering in Smart Grid: Methodologies, Applications, and Future Trends

Wan

et al. 2021

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

With the increasingly widespread of advanced metering infrastructure, electric load clustering is becoming more essential for its great potential in analytics of consumers' energy consumption patterns and preference through data mining. Moreover, a variety of electric load clustering techniques have been put into practice to obtain the distribution of load data, observe the characteristics of load clusters, and classify the components of the total load. This can give rise to the development of related techniques and research in the smart grid, such as demand-side response. This paper summarizes the basic concepts and the general process in electric load clustering. Several similarity measurements and five major categories in electric load clustering are then comprehensively summarized along with their advantages and disadvantages. Afterwards, eight indices widely used to evaluate the validity of electric load clustering are described. Finally, vital applications are discussed thoroughly along with future trends including the tariff design, anomaly detection, load forecasting, data security and big data, etc.

show abstract

“…Time domain along with fluctuation characteristics was considered in [65] in the local modeling, where adaptive Kmeans was used to cluster the load curves.…”

Section: ) Adaptive K-meansmentioning

confidence: 99%

Electric Load Clustering in Smart Grid: Methodologies, Applications, and Future Trends

Wan

et al. 2021

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

show abstract

“…The traditional K‐means clustering algorithm randomly selects k samples as the initial mean vector

(μ_{1}, μ_{2}, \dots, μ_{7})

and takes

(μ_{1}, μ_{2}, \dots, μ_{7})

as the clustering centroids, then calculates the distance from each sample in the sample set

D = {x_{1}, x_{2}, \dots, x_{m}}

to the k clustering centroids respectively and divides each sample into the nearest clustering centroids. Eventually the algorithm recalculates the centroid of each cluster and repeatedly searches for the new centroid cluster until the centroid position no longer changes or reaches the maximum number of iteration rounds [15, 16]. However, due to the problem of local optimal of the K‐means algorithm and the low convexity of the channel state information of Wi‐Fi networks, it is difficult to make the optimal selection of retransmission mode.…”

Section: Intelligent Selection Algorithm For Retransmissionmentioning

confidence: 99%

Intelligent hybrid automatic repeat request retransmission for multi‐band Wi‐Fi networks

Wang

Fang

et al. 2021

IET Communications

View full text Add to dashboard Cite

With the increase of Wi-Fi connections and throughput, the problems posed by unreliable connection and unstable delay need to be urgently solved through advanced methods. The IEEE 802.11ax standard was designed to improve the reliability of connection and increase the throughput in dense Wi-Fi scenario where the inter-site interference becomes more serious and the spectral resources are more insufficient. Furthermore, in the next generation of Wi-Fi standard IEEE 802.11be, the hybrid automatic repeat request (HARQ) and multi-band technologies will be introduced to solve the problems of unstable delay, serious interference and insufficient spectrum resources. In this paper, retransmission schemes combined multi-band and hybrid automatic repeat request are deeply studied to improve the retransmission efficiency. Combining hybrid automatic repeat request with multi-band, this paper proposes three kinds of retransmission mode: insisting on the current frequency band (ICFB) retransmission, switching to the backup frequency band (SBFB) retransmission and concurrent retransmitting on dual-band (CRDB). Then, an intelligent selection algorithm of retransmission mode based on machine learning is designed to determine the optimal mode. Theoretical analysis and experimental results show that the proposed method can greatly improve the retransmission efficiency and thereby reduce the transmission latency and increase the throughput for multi-band Wi-Fi networks.

show abstract

“…For instance, one of the well‐known centroid‐based algorithms is K ‐means. In a nutshell, K ‐means determines K centroids in the data and clusters points by assigning them to the nearest centroid [41, 42]. Even though K ‐means is easy to understand and implement, the algorithm has no notion of outliers.…”

Section: Integration Of Ai Techniques Into the Developed Modelmentioning

confidence: 99%

Intelligent approach for residential load scheduling

Sadat-Mohammadi

Nazari-Heris

Nazerfard

et al. 2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Electricity consumption behaviour analysis based on adaptive weighted‐feature K‐means‐AP clustering

Cited by 15 publications

References 26 publications

Electric Load Clustering in Smart Grid: Methodologies, Applications, and Future Trends

Electric Load Clustering in Smart Grid: Methodologies, Applications, and Future Trends

Intelligent hybrid automatic repeat request retransmission for multi‐band Wi‐Fi networks

Intelligent approach for residential load scheduling

Contact Info

Product

Resources

About