14,435 research outputs found
Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data
Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a dataâdriven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the householdâlevel water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Timeâofâuse and intensityâofâuse differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201
Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler-Norway
The development of Smart Grid in Norway in specific and Europe/US in general
will shortly lead to the availability of massive amount of fine-grained
spatio-temporal consumption data from domestic households. This enables the
application of data mining techniques for traditional problems in power system.
Clustering customers into appropriate groups is extremely useful for operators
or retailers to address each group differently through dedicated tariffs or
customer-tailored services. Currently, the task is done based on demographic
data collected through questionnaire, which is error-prone. In this paper, we
used three different clustering techniques (together with their variants) to
automatically segment electricity consumers based on their consumption
patterns. We also proposed a good way to extract consumption patterns for each
consumer. The grouping results were assessed using four common internal
validity indexes. We found that the combination of Self Organizing Map (SOM)
and k-means algorithms produce the most insightful and useful grouping. We also
discovered that grouping quality cannot be measured effectively by automatic
indicators, which goes against common suggestions in literature.Comment: 12 pages, 3 figure
Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.
This report gives an overview of the most relevant organisational and\ud
behavioural aspects regarding user profiling. It discusses not only the\ud
most important aims of user profiling from both an organisationâs as\ud
well as a userâs perspective, it will also discuss organisational motives\ud
and barriers for user profiling and the most important conditions for\ud
the success of user profiling. Finally recommendations are made and\ud
suggestions for further research are given
Application of artificial neural network in market segmentation: A review on recent trends
Despite the significance of Artificial Neural Network (ANN) algorithm to
market segmentation, there is a need of a comprehensive literature review and a
classification system for it towards identification of future trend of market
segmentation research. The present work is the first identifiable academic
literature review of the application of neural network based techniques to
segmentation. Our study has provided an academic database of literature between
the periods of 2000-2010 and proposed a classification scheme for the articles.
One thousands (1000) articles have been identified, and around 100 relevant
selected articles have been subsequently reviewed and classified based on the
major focus of each paper. Findings of this study indicated that the research
area of ANN based applications are receiving most research attention and self
organizing map based applications are second in position to be used in
segmentation. The commonly used models for market segmentation are data mining,
intelligent system etc. Our analysis furnishes a roadmap to guide future
research and aid knowledge accretion and establishment pertaining to the
application of ANN based techniques in market segmentation. Thus the present
work will significantly contribute to both the industry and academic research
in business and marketing as a sustainable valuable knowledge source of market
segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table
Perceived congruence and online loyalty as segmentation variables in multichannel retailing: a comparison between appparel and electronics
As the interest of the literature on congruity between offline and online stores is relatively recent, empirical evidence is required to help marketing managers choose the most effective ways of contributing to the formation of consistent offerings as well as their contribution to generate customer loyalty. This study examines whether congruity can help to identify segments of heterogeneous consumers that differ significantly regarding these variables as well as other constructs related to the customer relationship with the retailer. The study attempts to identify which congruity attribute(s) are most relevant for differentiating customers by their loyalty towards the online store, so that retailers can design strategies for improving congruity between physical and online stores, and ultimately, increase online store loyalty
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