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Title:
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Identification and visualization of energy-consumption patters
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Author(s):
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Published by:
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Publication date:
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ECN
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1-10-1998
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ECN report number:
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Document type:
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ECN-RX--98-058
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Article (scientific)
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Number of pages:
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10
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Published in: Paper, presented at the Distributech DA/DSM Europe 1998 conference, London, United Kingdom, 27-29 October 1998 (), , , Vol., p.-.
Abstract:
The liberalisation of the energy markets will change the relationshipsbetween distribution company and customers. To take full advantage of future
differentiation in tariff structures, a better understanding of the
consumption patterns is required. The distribution company needs this insight
to segment its market and improve load profiles, whereas the customer needs
it to lower his electric bill by negotiation of a lower tariff, peak shaving,
and reducing and leveling consumption. The aims of our project are: (1) to
extract as much information as possible from data sets on electricity
consumption, (2) to identify typical consumption patterns, and (3) to
elucidate external factors that can explain these patterns. The development
and evaluation of methods, tools, and techniques for load profiling is an
important part thereof. Data sets were collected on the electricity
consumption in our institute, which is a mixture of laboratories and offices,
populated by 850 employees. Three different techniques were used:
visualization, cluster analysis, and multiple regression analysis. The
electricity consumption data of 1997, collected at 15-minute intervals, were
visualized in a single image using software developed in-house. A prototype
of an interactive time-series analysis tool with a calendar-based user
interface was developed, which supports cluster analysis of the site
consumption patterns. With this cluster analysis dominant patterns as well as
typical outliers can be identified easily. Multiple linear regression
analysis of the consumption data showed relations between the actual
electricity consumption and the number of employees present at that time; the
level of natural light; the day of the week; and the time of the day. Each
technique provides its own perspective; together they lead to a better
understanding of energy consumption patterns and effective demand side
management measures. 1 ref.
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