39,956 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
Energy demand prediction for the implementation of an energy tariff emulator to trigger demand response in buildings
Buildings are key actors of the electrical gird. As such they have an important role to play in grid
stabilization, especially in a context where renewable energies are mandated to become an increasingly
important part of the energy mix. Demand response provides a mechanism to reduce or displace electrical
demand to better match electrical production. Buildings can be a pool of flexibility for the grid to operate
more efficiently. One of the ways to obtain flexibility from building managers and building users is the
introduction of variable energy prices which evolve depending on the expected load and energy generation.
In the proposed scenario, the wholesale energy price of electricity, a load prediction, and the elasticity of
consumers are used by an energy tariff emulator to predict prices to trigger end user flexibility. In this paper,
a cluster analysis to classify users is performed and an aggregated energy prediction is realised using Random
Forest machine learning algorithm.This paper is part of a project that has received funding
from the European Unionâs Horizon 2020 research and
innovation programme under grant agreement No
768614. This paper reflects only the authorÂŽs views and
neither the Agency nor the Commission are responsible
for any use that may be made of the information contained
therein
Metadata for Energy Disaggregation
Energy disaggregation is the process of estimating the energy consumed by
individual electrical appliances given only a time series of the whole-home
power demand. Energy disaggregation researchers require datasets of the power
demand from individual appliances and the whole-home power demand. Multiple
such datasets have been released over the last few years but provide metadata
in a disparate array of formats including CSV files and plain-text README
files. At best, the lack of a standard metadata schema makes it unnecessarily
time-consuming to write software to process multiple datasets and, at worse,
the lack of a standard means that crucial information is simply absent from
some datasets. We propose a metadata schema for representing appliances,
meters, buildings, datasets, prior knowledge about appliances and appliance
models. The schema is relational and provides a simple but powerful inheritance
mechanism.Comment: To appear in The 2nd IEEE International Workshop on Consumer Devices
and Systems (CDS 2014) in V\"aster{\aa}s, Swede
The Measurement of the Energy Intensity of Manufacturing Industries: A Principal Components Analysis
Energy intensity is the ratio of energy use to output. Most industries deal with several energy sources and outputs. This leads to the usual difficulties of aggregating heterogeneous inputs and outputs. We apply principal components analysis to assess the information derived from six energy intensity indicators. We use two measures of total energy use (thermal and economic) and three measures of industry output (value added, value of production, and value of shipments). The data comes from manufacturing industries in Québec, Ontario, Alberta, and British Columbia from 1976 to 1996. We find that the variation of the six energy intensity indicators that is accounted for by the first principal component is quite large. However, depending on how variables are measured, there may be significant differences in the assessment of the evolution of energy intensity for some industries. There are no particular patterns in this respect. This makes identifying benchmarks that could be used to assess future performance difficult.Energy intensity; aggregation; principal components analysis
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