Load profiling and its applications in power market

Chang, Rung-Fang; Lü, Chao

doi:10.1109/pes.2003.1270442

Cited by 27 publications

(15 citation statements)

References 4 publications

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“…Clustering usage patterns makes it possible to identify typical behaviors called typical load profiles (TLPs) [45,46]. TLPs could then be used for load forecasting [47,48], load estimation [49], load control [50], abnormal electricity consumption detection [51], designing electricity tariff offers [52], developing market strategies [53] or demand side response policy [54]. Some of the most widely used metering intelligence activities are discussed below.…”

Section: B Tools For Smart Meteringmentioning

confidence: 99%

Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

Alahakoon

2016

IEEE Trans. Ind. Inf.

477

170

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Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics which deal with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of metering process, the different stakeholder interests and technologies used to satisfy stakeholder interests. Furthermore the paper highlights the challenges as well as opportunities arising due to the advent of big data and the increasing popularity of the cloud environments.time pricing schemes, and demand-response for energy saving and efficient use of energy generated.The research and development in smart meters and their applications have been progressed rapidly in recent years and many methods and techniques have been developed. The technological scope relating to smart meters covers a diverse range of ICT technologies, such as electronics and communication, and there have already been several surveys done, e.g.[7] looks into the different applications of smart grids focusing on the communication needs and also on communication requirements of smart meters for integration into power grids.[8] is a comprehensive survey of communication technologies for smart meters which could be used to satisfy the identified needs.[9] introduces a novel smart meter communication technology, [10] examines the web and data service aspect of smart meter networks, and [11] proposes a framework for smart meter privacy. In this paper, we examine these developments from a holistic data analytic viewpoint. We will also outline potential future applications and challenges that lie ahead. As a foundation for our holistic approach the key components of electricity meter intelligence is shown as in Figure 1. The three key components capture the aspects of data, technology and stakeholders. These aspects and the role they play in smart meter intelligence is discussed in section 2.

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Section: B Tools For Smart Meteringmentioning

confidence: 99%

Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

Alahakoon

2016

IEEE Trans. Ind. Inf.

477

170

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“…The processing of the collected load data can lead to the determination of consumers' load profiles [8]. The term "load profiling" refers to the formulation of representative load curves over a given time period of a single consumer or groups of consumers [9][10][11]. The representative load curves or load profiles are actually the averaged load curves that have been grouped together in the same cluster.…”

Section: Motivationmentioning

confidence: 99%

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Panapakidis

Christoforidis

2018

Applied Sciences

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Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA) and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.

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“…A procedure for determining typical load profiles based on fuzzy C-means methods is represented in [6]. While load profiling is discussed in [13] by using fuzzy C-means and decision tree, a hybrid fuzzy C-means and artificial neural network approach is addressed in [14]. G. Chicco and his colleagues have presented several papers such as [7,[15][16][17][18] which discuss different clustering techniques for load curve classification including K-means, hierarchical algorithms, modified follow the leader, etc.…”

Section: Literature Reviewmentioning

confidence: 99%