The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
Lilia Filipova-neumann - One of the best experts on this subject based on the ideXlab platform.
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Cluster Analysis of Smart Metering Data
WIRTSCHAFTSINFORMATIK, 2012Co-Authors: Christoph Flath, Clemens Van Dinther, Clemens Dinther, David Nicolay, Tobias Conte, Lilia Filipova-neumannAbstract:The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a Cluster Analysis of smart metering data focused on the customers' time-based consumption behavior allows for a detailed customer segmentation. In the article we present a Cluster Analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a Cluster Analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the Cluster Analysis and highlight options to apply them to segment-specific tariff design.
Christoph Flath - One of the best experts on this subject based on the ideXlab platform.
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Cluster Analysis of smart metering data
Web Intelligence, 2012Co-Authors: Christoph Flath, David Nicolay, Tobias Conte, Clemens Van Dinthe, Lilia FilipovaneumaAbstract:The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a Cluster Analysis of smart metering data focused on the customers’ time-based consumption behavior allows for a detailed customer segmentation. In the article we present a Cluster Analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a Cluster Analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the Cluster Analysis and highlight options to apply them to segment-specific tariff design. Copyright Gabler Verlag 2012
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Cluster Analysis of Smart Metering Data
WIRTSCHAFTSINFORMATIK, 2012Co-Authors: Christoph Flath, Clemens Van Dinther, Clemens Dinther, David Nicolay, Tobias Conte, Lilia Filipova-neumannAbstract:The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a Cluster Analysis of smart metering data focused on the customers' time-based consumption behavior allows for a detailed customer segmentation. In the article we present a Cluster Analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a Cluster Analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the Cluster Analysis and highlight options to apply them to segment-specific tariff design.
David Nicolay - One of the best experts on this subject based on the ideXlab platform.
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Cluster Analysis of smart metering data
Web Intelligence, 2012Co-Authors: Christoph Flath, David Nicolay, Tobias Conte, Clemens Van Dinthe, Lilia FilipovaneumaAbstract:The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a Cluster Analysis of smart metering data focused on the customers’ time-based consumption behavior allows for a detailed customer segmentation. In the article we present a Cluster Analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a Cluster Analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the Cluster Analysis and highlight options to apply them to segment-specific tariff design. Copyright Gabler Verlag 2012
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Cluster Analysis of Smart Metering Data
WIRTSCHAFTSINFORMATIK, 2012Co-Authors: Christoph Flath, Clemens Van Dinther, Clemens Dinther, David Nicolay, Tobias Conte, Lilia Filipova-neumannAbstract:The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a Cluster Analysis of smart metering data focused on the customers' time-based consumption behavior allows for a detailed customer segmentation. In the article we present a Cluster Analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a Cluster Analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the Cluster Analysis and highlight options to apply them to segment-specific tariff design.
Tobias Conte - One of the best experts on this subject based on the ideXlab platform.
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Cluster Analysis of smart metering data
Web Intelligence, 2012Co-Authors: Christoph Flath, David Nicolay, Tobias Conte, Clemens Van Dinthe, Lilia FilipovaneumaAbstract:The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a Cluster Analysis of smart metering data focused on the customers’ time-based consumption behavior allows for a detailed customer segmentation. In the article we present a Cluster Analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a Cluster Analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the Cluster Analysis and highlight options to apply them to segment-specific tariff design. Copyright Gabler Verlag 2012
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Cluster Analysis of Smart Metering Data
WIRTSCHAFTSINFORMATIK, 2012Co-Authors: Christoph Flath, Clemens Van Dinther, Clemens Dinther, David Nicolay, Tobias Conte, Lilia Filipova-neumannAbstract:The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a Cluster Analysis of smart metering data focused on the customers' time-based consumption behavior allows for a detailed customer segmentation. In the article we present a Cluster Analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a Cluster Analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the Cluster Analysis and highlight options to apply them to segment-specific tariff design.
Clemens Van Dinther - One of the best experts on this subject based on the ideXlab platform.
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Cluster Analysis of Smart Metering Data
WIRTSCHAFTSINFORMATIK, 2012Co-Authors: Christoph Flath, Clemens Van Dinther, Clemens Dinther, David Nicolay, Tobias Conte, Lilia Filipova-neumannAbstract:The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a Cluster Analysis of smart metering data focused on the customers' time-based consumption behavior allows for a detailed customer segmentation. In the article we present a Cluster Analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a Cluster Analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the Cluster Analysis and highlight options to apply them to segment-specific tariff design.
