2,002 research outputs found
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
Local Short Term Electricity Load Forecasting: Automatic Approaches
Short-Term Load Forecasting (STLF) is a fundamental component in the
efficient management of power systems, which has been studied intensively over
the past 50 years. The emerging development of smart grid technologies is
posing new challenges as well as opportunities to STLF. Load data, collected at
higher geographical granularity and frequency through thousands of smart
meters, allows us to build a more accurate local load forecasting model, which
is essential for local optimization of power load through demand side
management. With this paper, we show how several existing approaches for STLF
are not applicable on local load forecasting, either because of long training
time, unstable optimization process, or sensitivity to hyper-parameters.
Accordingly, we select five models suitable for local STFL, which can be
trained on different time-series with limited intervention from the user. The
experiment, which consists of 40 time-series collected at different locations
and aggregation levels, revealed that yearly pattern and temperature
information are only useful for high aggregation level STLF. On local STLF
task, the modified version of double seasonal Holt-Winter proposed in this
paper performs relatively well with only 3 months of training data, compared to
more complex methods
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