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Clustering Household Electricity Use Profiles

https://doi.org/10.1145/2542652.2542656
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9780470316801openalex-snapshot
Finding Groups in Data
inferredcites →https://doi.org/10.1007/978-3-642-04921-7_41openalex-snapshot
Feature-Based Clustering for Electricity Use Time Series Data
inferredcites →https://doi.org/10.1007/978-3-642-55721-7_12openalex-snapshot
On The General Distance Measure
inferredcites →https://doi.org/10.1007/978-3-642-56927-2openalex-snapshot
Self-Organizing Maps
inferredcites →https://doi.org/10.1007/s00180-009-0178-4openalex-snapshot
KmL: k-means for longitudinal data
inferredcites →https://doi.org/10.1007/s10618-005-0039-xopenalex-snapshot
Characteristic-Based Clustering for Time Series Data
inferredcites →https://doi.org/10.1016/0377-0427(87)90125-7openalex-snapshot
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
inferredcites →https://doi.org/10.1016/j.energy.2011.12.031openalex-snapshot
Overview and performance assessment of the clustering methods for electrical load pattern grouping
inferredcites →https://doi.org/10.1016/j.enpol.2012.06.062openalex-snapshot
Consumer responses to time varying prices for electricity
inferredcites →https://doi.org/10.1016/j.neucom.2008.10.017openalex-snapshot
Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series
inferredcites →https://doi.org/10.1023/a:1012801612483openalex-snapshot
On Clustering Validation Techniques
inferredcites →https://doi.org/10.1080/01621459.1963.10500845openalex-snapshot
Hierarchical Grouping to Optimize an Objective Function
inferredcites →https://doi.org/10.1080/01969727408546059openalex-snapshot
Well-Separated Clusters and Optimal Fuzzy Partitions
inferredcites →https://doi.org/10.1080/03610917408548446openalex-snapshot
A Dendrite Method for Cluster Analysis
inferredcites →https://doi.org/10.1080/03610927408827101openalex-snapshot
A dendrite method for cluster analysis
inferredcites →https://doi.org/10.1093/bioinformatics/btr406openalex-snapshot
APCluster: an R package for affinity propagation clustering
inferredcites →https://doi.org/10.1108/ilds.2002.12230dab.004openalex-snapshot
Oxford University Press
inferredcites →https://doi.org/10.1109/tpami.1979.4766909openalex-snapshot
A Cluster Separation Measure
inferredcites →https://doi.org/10.1126/science.1136800openalex-snapshot
Clustering by Passing Messages Between Data Points
inferredcites →https://doi.org/10.1175/1520-0477(1982)063<1309:scoteo>2.0.co;2openalex-snapshot
Some Comments on the Evaluation of Model Performance
inferredcites →https://doi.org/10.1214/aos/1069362301openalex-snapshot
The integrated periodogram for stable processes
inferredcites →https://doi.org/10.18637/jss.v025.i04openalex-snapshot
clValid : An R Package for Cluster Validation
inferredcites →https://doi.org/10.2307/2669719openalex-snapshot
Neural, Novel & Hybrid Algorithms for Time Series Prediction
inferredcites →https://doi.org/10.5860/choice.41-3492openalex-snapshot
Chaos and time-series analysis

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4780333openalex-extracted
Sustainable Energy Consumption: Empirical Evidence of a Household in Poland
inferred← citeshttps://doi.org/10.1007/978-3-319-73830-7_7openalex-snapshot
Load Pattern Shape Clustering Analysis for Manufacturing
inferred← citeshttps://doi.org/10.1007/978-981-97-3523-5_3openalex-snapshot
Probabilistic Forecasting Analysis on Electric Load Systems
inferred← citeshttps://doi.org/10.1007/s10618-019-00647-xopenalex-snapshot
catch22: CAnonical Time-series CHaracteristics
inferred← citeshttps://doi.org/10.1016/j.compenvurbsys.2016.06.003openalex-snapshot
Electricity consumption and household characteristics: Implications for census-taking in a smart metered future
inferred← citeshttps://doi.org/10.1016/j.enbuild.2019.04.026openalex-snapshot
Identifying residential daily electricity-use profiles through time-segmented regression analysis
inferred← citeshttps://doi.org/10.1016/j.enbuild.2022.112508openalex-snapshot
A reduced-dimension feature extraction method to represent retail store electricity profiles
inferred← citeshttps://doi.org/10.1016/j.rser.2017.07.010openalex-snapshot
Smart grid research in New Zealand – A review from the GREEN Grid research programme
inferred← citeshttps://doi.org/10.1016/j.segan.2022.100849openalex-snapshot
Which internal validation indices to use while clustering electric load demand profiles?
inferred← citeshttps://doi.org/10.1109/seaa53835.2021.00031openalex-snapshot
An Approach for Ranking Feature-based Clustering Methods and its Application in Multi-System Infrastructure Monitoring
inferred← citeshttps://doi.org/10.1109/sest50973.2021.9543285openalex-snapshot
Impact of Load Demand Dataset Characteristics on Clustering Validation Indices
inferred← citeshttps://doi.org/10.1109/tii.2021.3061470openalex-snapshot
Validating Clustering Frameworks for Electric Load Demand Profiles
inferred← citeshttps://doi.org/10.1109/tpwrs.2014.2377213openalex-snapshot
Impacts of Raw Data Temporal Resolution Using Selected Clustering Methods on Residential Electricity Load Profiles
inferred← citeshttps://doi.org/10.1145/3748726openalex-snapshot
On the Use of Relative Validity Indices for Comparing Clustering Approaches
inferred← citeshttps://doi.org/10.48550/arxiv.2103.00030openalex-snapshot
Validating Clustering Frameworks for Electric Load Demand Profiles
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