Electricity consumption pattern analysis beyond traditional clustering methods: A novel self-adapting semi-supervised clustering method and application case study

Zhang, Xiaohai; Ramírez-Mendiola, José Luis; Li, Mingtao; Guo, Liejin

doi:10.1016/j.apenergy.2021.118335

Cited by 25 publications

(6 citation statements)

References 32 publications

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“…The process concludes with a set of identified exemplars that signify the center of each cluster and the corresponding association of individual data points to these clusters. In power systems, Affinity Propagation can be used in identifying patterns [396], [397] or groupings within smart grid data, such as usage patterns across various customer segments or the identification of similar performance metrics across different regions of the grid, thereby aiding in more efficient system management and resource allocation [398], [399], [400].…”

Section: ) Affinity Propagationmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

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Section: ) Affinity Propagationmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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“…Ref. [31] developed a semi-supervised automatic clustering algorithm based on a self-adapting metric learning process for determining the household electricity consumers demand patterns.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…(28). From (28), the chosen metric criteria , ω k * ,u * ,t , is defined as sum of the average weights of bidding and offering elements including bid and offer prices, energy quantity, location of the prosumers on the local community for bids and offers clusters combination of c b k * ,t and c s u * ,t , respectively, which are given in (31):…”

Section: Subject Tomentioning

confidence: 99%

“…From (31), ω p k * ,u * ,t is the average price weight for possible matching bidding cluster, c b k * ,t , and offering cluster, c s u * ,t , as represented in the price weight matrix of (32) and given in (38).…”

Section: Subject Tomentioning

confidence: 99%

“…In this way, the average weights calculated by Eq. ( 38) will be the inputs of (31). After prosumers are clustered in bid and offer clusters separately, P2P matching occur among prosumers which their bids and offers belong to the selected clusters.…”

Section: Subject Tomentioning

confidence: 99%

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Advanced Clustering Approach for Peer-to-Peer Local Energy Markets Considering Prosumers’ Preference Vectors

Okwuibe

Gazafroudi²,

Mengelkamp³

et al. 2023

IEEE Access

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Local energy markets (LEMs) are utilized in a bottom-up power systems approach for reducing the complexity of the traditional, centralized power system and to enable better integration of decentralized renewable energy resources (RES). Peer-to-peer (P2P) energy trading creates opportunities for prosumers to trade their RES with other prosumers in the LEM. Although several scenarios were proposed in the literature for modelling P2P energy trading, there is still a gap in the literature considering the heterogeneous characteristics of prosumers' bidding preferences during P2P matching in the LEM. In this paper, we present heterogeneous characteristics of bidding preferences for prosumers considering energy quantity, bid/offer price, geographic location, location of agents on the local community and cluster welfare. Moreover, this paper proposes an advanced clustering model for P2P matching in the energy community considering the heterogeneous characteristics of bidding preferences for prosumers. For evaluating our proposed model performance, two German real case scenarios of a small and large communities were studied. The simulations results show that using price preference, as the criterion for clustering, offers more technical and economic benefits to energy communities compared to other clustering scenarios. On the other hand, clustering scenarios based on location of prosumers ensure that energy is traded among prosumers who are closer to each other.INDEX TERMS Energy community, advanced clustering, local energy market, matching mechanism, Peerto-peer trading.

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Advances in Deep Learning Techniques for Short-term Energy Load Forecasting Applications: A Review

Chandrasekaran,

Paramasivan

2024

Arch Computat Methods Eng

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Electricity consumption pattern analysis beyond traditional clustering methods: A novel self-adapting semi-supervised clustering method and application case study

Cited by 25 publications

References 32 publications

K-Means and Alternative Clustering Methods in Modern Power Systems

K-Means and Alternative Clustering Methods in Modern Power Systems

Advanced Clustering Approach for Peer-to-Peer Local Energy Markets Considering Prosumers’ Preference Vectors

Advances in Deep Learning Techniques for Short-term Energy Load Forecasting Applications: A Review

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