Application of load survey systems to proper tariff design

Chen, C.S.; Hwang, Jau-Chung; Huang, C.W.

doi:10.1109/59.627886

Cited by 129 publications

(31 citation statements)

References 4 publications

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“…However, with the Automatic Meter Reading (AMR) widely deployed it became relatively easier to gather data about many consumers. The electricity load characteristics are being considered in many areas: for example, in performing demand forecast [2,3,8], in computing price elasticity [5], and in designing of DSM and electricity tariffs [6], and in managing various assets [8]. In [4] the temperature sensitivity of load is studied for sample consumers who are selected considering the population of commercial, industrial and residential consumers in Taiwan.…”

Section: Introductionmentioning

confidence: 99%

The Temperature Sensitivity of the Commercial Load in KOREA

Yoon¹,

Kang²,

Yoon³

2013

Journal of International Council on Electrical Engineering

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Section: Introductionmentioning

confidence: 99%

The Temperature Sensitivity of the Commercial Load in KOREA

Yoon¹,

Kang²,

Yoon³

2013

Journal of International Council on Electrical Engineering

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“…Consumers with low consumption variability use big appliances, such as water pump and washing machine, in a shorter duration. 5 Furthermore, consumers with high consumption variability use tumble dryers for the longest duration.…”

Section: Knowledge Extraction From Survey Datamentioning

confidence: 99%

“…Moss et al provided an overview of consumer segmentation in the electricity sector, especially for demand-side management [15]. Application-specific segmentations frameworks have been developed in [5,6,7,10,11,19,21], mainly for setting tariff or consumer classification, and predicting consumer characteristics [3].…”

Section: Related Workmentioning

confidence: 99%

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Consumer Segmentation and Knowledge Extraction from Smart Meter and Survey Data

Wijaya¹,

Ganu²,

Chakraborty³

et al. 2014

Proceedings of the 2014 SIAM International Conference on Data Mining

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Many electricity suppliers around the world are deploying smart meters to gather fine-grained spatiotemporal consumption data and to effectively manage the collective demand of their consumer base. In this paper, we introduce a structured framework and a discriminative index that can be used to segment the consumption data along multiple contextual dimensions such as locations, communities, seasons, weather patterns, holidays, etc. The generated segments can enable various higher-level applications such as usagespecific tariff structures, theft detection, consumer-specific demand response programs, etc. Our framework is also able to track consumers' behavioral changes, evaluate different temporal aggregations, and identify main characteristics which define a cluster.

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“…All the above methods generally belong to pattern recognition techniques (Theodoridis & Koutroumbas, 1999). Alternatively, classification problem can be solved by using data mining (Kitayama et al, 2003;Figueiredo et al, 2005), wavelet packet transformation (Petrescu & Scutariu, 2002), frequency-domain data (Carpaneto et al, 2006), stratified sampling (Chen et al, 1997). For the reduction of the size of the clustering input data set Sammon map, principal component analysis and curvilinear component analysis have been proposed .…”

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confidence: 99%