Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications

Wang, Yi; Chen, Qixin; Chen, Kang; Xia, Qing

doi:10.1109/tsg.2016.2548565

Cited by 289 publications

(118 citation statements)

References 38 publications

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“…Utilizing machine learning techniques to detect distinct energy consumption patterns of customers and select high-quality customers for energy programs (e.g., demand response programs) is becoming more and more popular in addressing competitive utility companies and the future energy business ecosystem [84][85][86][87].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…Together with various types of machine learning techniques, including successful application of supervised learning in demand response targeting [85] and unsupervised learning in individualized pricing design [87], reinforcement learning (RL) is also believed to have the potential to deal with the energy trading problem and guide energy entities to interact with the market environment. The most important feature distinguishing RL from other types of learning is that it uses training information that evaluates the actions taken rather than instructions by giving correct actions [88].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…Agent-based simulation is mostly combined with machine learning techniques to facilitate the interaction dynamics among different agent entities [82]. The machine learning technology also frequently uses optimization methods to train its model parameters and hyper-parameters [85]. Last but not least, the summary of each individual modeling method is shown in Table 1.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

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The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

et al. 2017

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Due to the rapidly-changing technologies in the power industry, many new references addressing the frameworks and business models of the next-generation retail electricity market are entering the research community. In particular, considering new customers with considerable demand response awareness and so-called prosumers with localized power generation based on distributed energy resources (DERs), the next-generation retail electricity market infrastructure will be a level playing field for local energy transactions, strategic pricing scheme design, new business model design and building an innovative energy ecosystem. Consequently, there is an urgent need to keep track of international experiences and activities taking place in the field of the market mechanism design problem at the distribution level. This paper provides a comprehensive survey of recent technology developments and aims to inspire awareness of the further deregulation of the electricity market, especially in areas close to customers. We mainly bring attention to the more than 90 articles published during the past five years. The collected literature has been divided into different sections to discuss different aspects of the next-generation retail electricity market under the deregulated power industry.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Section: Machine Learning Techniquesmentioning

confidence: 99%

Section: Machine Learning Techniquesmentioning

confidence: 99%

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The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

et al. 2017

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“…The second type of standardization is the one suggested in [24] who used the same Irish data set. It consists of transforming the values of the vector x id to the range of [0, 1] as the following:…”

Section: Data Normalizationmentioning

confidence: 99%

A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

Melzi

Samé²,

Zayani

et al. 2017

Energies

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Abstract:The large amount of data collected by smart meters is a valuable resource that can be used to better understand consumer behavior and optimize electricity consumption in cities. This paper presents an unsupervised classification approach for extracting typical consumption patterns from data generated by smart electric meters. The proposed approach is based on a constrained Gaussian mixture model whose parameters vary according to the day type (weekday, Saturday or Sunday). The proposed methodology is applied to a real dataset of Irish households collected by smart meters over one year. For each cluster, the model provides three consumption profiles that depend on the day type. In the first instance, the model is applied on the electricity consumption of users during one month to extract groups of consumers who exhibit similar consumption behaviors. The clustering results are then crossed with contextual variables available for the households to show the close links between electricity consumption and household socio-economic characteristics. At the second instance, the evolution of the consumer behavior from one month to another is assessed through variations of cluster sizes over time. The results show that the consumer behavior evolves over time depending on the contextual variables such as temperature fluctuations and calendar events.

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“…For example, authors in [28] perform clustering on household occupancy states which have been inferred using a Hidden Markov model. In a similar vein, authors in [29] apply Fast Search and Find of Density Peaks [30], a novel density-based clustering method, on occupancy state transition matrices. Locality-sensitive hashing is used in [31] to substantially speed up subsequent similarity comparisons for clustering.…”

mentioning

confidence: 99%

C-Vine Copula Mixture Model for Clustering of Residential Electrical Load Pattern Data

Sun

Konstantelos

Štrbac

2017

IEEE Trans. Power Syst.

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Abstract-The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas towards identifying multi-dimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2,613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method.Index Terms-Clustering, customer classification, C-vine, decision trees, mixture models, pair-copula construction, smart meters. I. INTRODUCTIONlectricity market liberalization has largely unbundled the distribution and supply services in many jurisdictions, providing customers with the freedom to select their electricity supplier. In this competitive environment, retail companies can improve the commercial attractiveness of their product by formulating tariffs aimed at different customer types. An important part of the tariff design process is the identification of meaningful customer classes that exhibit different consumption patterns, enabling the development of diversifiable products. Moreover, electrical customer classification can also play a crucial role in load forecasting [1][2] and modeling [3], electricity market development [4], energy system planning and operation [5] and theft detection [6]. Naturally, information on customer type (e.g. industrial, commercial, residential) provides important information regarding the likely electricity consumption pattern and intensity. However, for further partitioning and exploratory analysis to be carried out effectively, high-frequency demand measurements are necessary [7]. As such, the advent of smart metering has led to large-scale availability of consumption data that render clustering analysis increasingly possible.Load profile clustering aims to allocate consumers into a small number of homogeneous groups, ensuring that elements of the same cluster are similar between them, while being dissimilar to elements of different clusters. A large number of clustering techniques have been proposed in the past and applied to electrical load ...

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Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications

Cited by 289 publications

References 38 publications

The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

C-Vine Copula Mixture Model for Clustering of Residential Electrical Load Pattern Data

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