Electric energy customer characterisation for developing dedicated market strategies

Chicco, Gianfranco; Napoli, Roberto; Postolache, Petru; Scutariu, M.; Toader, Cornel

doi:10.1109/ptc.2001.964627

Cited by 28 publications

(22 citation statements)

References 5 publications

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“…However, the question how to allocate the TLPs to the representative groups of consumers remains when dealing with the pattern-recognition methods as a load profile modeling tool. Although in [5] and [8] the authors propose an approach of allocating the TLPs based on the observations of the individual consumers LP for a certain period, we believe that such methodology is still time consuming and expensive.…”

Section: Introductionmentioning

confidence: 99%

Allocation of the Load Profiles to Consumers Using Probabilistic Neural Networks

Gerbec

Gasperic

Smon

et al. 2005

IEEE Trans. Power Syst.

136

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The new emerged operating conditions in the power sector are forcing the power-market participants to develop new tools. Among them, load profiles are a key issue in retail power markets. For various types of small consumers without quarterhourly load measurements, determination of typical load profiles (TLPs) could serve as a tool for determining of their load diagrams. Their main function is in billing of consumers who have deviated from their contracted schedules. Moreover, a simple and straightforward method for assigning a TLP to a particular eligible consumer also needs to be established. In this paper, a methodology for allocating consumers' load profiles using probabilistic neural network (PNN) is presented. It is based on the preprocessed measured load profiles (MLPs), using wavelet multiresolution analysis, clustered with a FCM clustering algorithm with an appropriate cluster-validity measure. The results demonstrate the efficiency of the formation procedure for the proposed TLPs.Index Terms-Cluster analysis, fuzzy logic, load profiles, power distribution, probabilistic neural networks, wavelet analysis.

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Section: Introductionmentioning

confidence: 99%

Allocation of the Load Profiles to Consumers Using Probabilistic Neural Networks

Gerbec

Gasperic

Smon

et al. 2005

IEEE Trans. Power Syst.

136

View full text Add to dashboard Cite

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“…More refined indices that take into account weekly patterns (working days and weekends) can be added, as those described in Chicco et al [18].…”

Section: Data Preprocessing and Feature Extractionmentioning

confidence: 99%

Embedded Cyber-Physical Anomaly Detection in Smart Meters

Raciti

Nadjm‐Tehrani

2013

Critical Information Infrastructures Security

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Abstract. Smart grid security has many facets, ranging over a spectrum from resisting attacks aimed at supervisory and control systems, to end user privacy concerns while monitored by the utility enterprise. This multi-faceted problem also includes vulnerabilities that arise from deployment of local cyber-physical attacks at a smart metering location, with a potential to a) manipulate the measured energy consumption, and b) being massively deployed aiming at destabilisation. In this paper we study a smart metering device that uses a trusted platform for storage and communication of metering data, and show that despite the hard core security, there is still room for deployment of a second level of defence as an embedded real-time anomaly detector that can cover both the cyber and physical domains.

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“…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. A fusion of fuzzy K-means and the expectation maximization algorithm method is discussed in [19].…”

Section: Literature Reviewmentioning

confidence: 99%