Lilia Filipova-Neumann

Cluster Analysis of Smart Metering Data

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.

Business Aspects of Web Services

Driven by maturing Web service technologies and the wide acceptance of the service-oriented architecture paradigm, the software industrys traditional business models and strategies have begun to change: software vendors are turning into service providers. In addition, in the Web service market, a multitude of small and highly specialized providers offer modular services of almost any kind and economic value is created through the interplay of various distributed service providers that jointly contribute to form individualized and integrated solutions. This trend can be optimally catalyzed by universally accessible service orchestration platforms service value networks (SVNs) which are the underlying organizational form of the coordination mechanisms presented in this book.Here, the authors focus on providing comprehensive business-oriented insights into todays trends and challenges that stem from the transition to a service-led economy. They investigate current and future Web service business models and provide a framework for Web service value networks. Pricing mechanism basics are introduced and applied to the specific area of SVNs. Strategies for platform providers are analyzed from the viewpoint of a single provider, and so are pricing mechanisms in service value networks which are optimal from a network perspective. The extended concept of pricing Web service derivatives is also illustrated. The presentation concludes with a vision of how Web service markets in the future could be structured and what further developments can be expected to happen.This book will be of interest to researchers in business development and practitioners such as managers of SMEs in the service sector, as well as computer scientists familiar with Web technologies. The books comprehensive content provides readers with a thorough understanding of the organizational, economic and technical implications of dealing with Web services as the nucleus of modern business models, which can be applied to Web services in general and Web service value networks specifically..

Clusteranalyse von Smart-Meter-Daten

ZusammenfassungDie Einführung der Smart-Meter-Technologie stellt die Energiewirtschaft in Deutschland vor große Herausforderungen. Neben hohen Investitionen in die Zähler- und Kommunikationsinfrastruktur ist auch die Neugestaltung vieler Geschäftsprozesse erforderlich. Da die neu entstehenden Kosten nur begrenzt an Endkunden übertragbar sind, gilt es die Aufwendungen der Energiewirtschaft durch neue Dienste und verbesserte Prozesse auf Basis von Smart Metering zu kompensieren. So ist durch die Clusteranalyse der detaillierteren Verbrauchsdaten eine deutlich feinere Kundensegmentierung auf Basis des zeitlichen Verbrauchsverhaltens möglich. Im Rahmen eines Smart-Metering-Projektes bei einem regionalen Energieversorger wurde eine Clusteranalyse für die real vorliegenden Kundenverbrauchsdaten entwickelt und in eine Business-Intelligence-Umgebung integriert. In diesem Beitrag beschreiben und evaluieren wir dieses Artefakt im Sinne der Design Science. Wir gehen dabei insbesondere auf die Ergebnisse der Clusteranalyse von Realdaten und den möglichen Einsatz zur segmentspezifischen Tarifgestaltung ein.AbstractThe 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.

Clusteranalyse von Smart-Meter-Daten@@@Cluster Analysis of Smart Metering Data: Eine praxisorientierte Umsetzung@@@An Implementation in Practice

ZusammenfassungDie Einführung der Smart-Meter-Technologie stellt die Energiewirtschaft in Deutschland vor große Herausforderungen. Neben hohen Investitionen in die Zähler- und Kommunikationsinfrastruktur ist auch die Neugestaltung vieler Geschäftsprozesse erforderlich. Da die neu entstehenden Kosten nur begrenzt an Endkunden übertragbar sind, gilt es die Aufwendungen der Energiewirtschaft durch neue Dienste und verbesserte Prozesse auf Basis von Smart Metering zu kompensieren. So ist durch die Clusteranalyse der detaillierteren Verbrauchsdaten eine deutlich feinere Kundensegmentierung auf Basis des zeitlichen Verbrauchsverhaltens möglich. Im Rahmen eines Smart-Metering-Projektes bei einem regionalen Energieversorger wurde eine Clusteranalyse für die real vorliegenden Kundenverbrauchsdaten entwickelt und in eine Business-Intelligence-Umgebung integriert. In diesem Beitrag beschreiben und evaluieren wir dieses Artefakt im Sinne der Design Science. Wir gehen dabei insbesondere auf die Ergebnisse der Clusteranalyse von Realdaten und den möglichen Einsatz zur segmentspezifischen Tarifgestaltung ein.AbstractThe 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.

Clusteranalyse von Smart-Meter-Daten : Cluster Analysis of Smart Metering Data - An Implementation in Practice

ZusammenfassungDie Einführung der Smart-Meter-Technologie stellt die Energiewirtschaft in Deutschland vor große Herausforderungen. Neben hohen Investitionen in die Zähler- und Kommunikationsinfrastruktur ist auch die Neugestaltung vieler Geschäftsprozesse erforderlich. Da die neu entstehenden Kosten nur begrenzt an Endkunden übertragbar sind, gilt es die Aufwendungen der Energiewirtschaft durch neue Dienste und verbesserte Prozesse auf Basis von Smart Metering zu kompensieren. So ist durch die Clusteranalyse der detaillierteren Verbrauchsdaten eine deutlich feinere Kundensegmentierung auf Basis des zeitlichen Verbrauchsverhaltens möglich. Im Rahmen eines Smart-Metering-Projektes bei einem regionalen Energieversorger wurde eine Clusteranalyse für die real vorliegenden Kundenverbrauchsdaten entwickelt und in eine Business-Intelligence-Umgebung integriert. In diesem Beitrag beschreiben und evaluieren wir dieses Artefakt im Sinne der Design Science. Wir gehen dabei insbesondere auf die Ergebnisse der Clusteranalyse von Realdaten und den möglichen Einsatz zur segmentspezifischen Tarifgestaltung ein.AbstractThe 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.

Services vs. Web Services

Without doubts, services have become the major driver of value creation in the last decades. This manifests in official statistics showing that services make up the largest part of the gross domestic product (GDP) in industrialized countries. In 2009, the share of the GDP within the European Union amounted to 71.9% and in the United States to 76.9% increasing steadily over last years. This trend is further amplified by the “servicification” of traditional products in many industries. According to Vargo and Lusch (2004), the major shift towards a service-centered view is driven by changes in society and markets that lead to exchanges of services rather than goods. It is not only stagnant product demand in many domains, but also the customers’ demand for customized and sophisticated goods which has pushed economic value downstream – away from manufacturing and toward the offering of services, both in preparing and customizing sales and in aftersales (Baumgartner and Wise 1999; Oliva and Kallenberg 2003). Driven by advancing Web service technologies, servicification in the software industry is a fundamental trend that tremendously changes the companies’ strategies and business models: software vendors become service providers (Dubey and Wagle 2007). The growing importance of automated service provision over the Web is impressively documented by the rise of platforms like Salesforce.

Coordination and Pricing in Service Value Networks: A Mechanism Design Approach

Weinhardt et al. (2003) and Neumann (2004) state that there is no general mechanism available to fit any possible market setting. In accordance with this statement, it is necessary to present a suitable mechanism designed to fit the underlying field of application. The adequacy of a mechanism depends, amongst others, on the properties of the trading objects. In SVNs, the latter are modular Web service components as well as the composed complex services resulting thereof – whose characteristics were discussed in detail in Chap. 3.

The Vision of Web Service Markets

Services become a central building block of value creation in today’s society. Novel technical, economic, and organizational challenges arise from their unique nature as services’ provision and consumption coincide in time (Hill 1977). Recognizing and understanding the importance of an efficient design, production, and provision of services under the presence of their special characteristics is inevitable for individuals and the society to compete in today’s global economy. Especially, rapid service innovation driven by the power of modularity that is inherent in the concept of services (Baldwin and Clark 2000) embodies the success factor in service-centric environments. However, when composing distributed service activities, the question of an efficient form of coordination comes to light and turns out to be fundamental to govern distributed value creation. As Web services are living artifacts that generally exist under the ownership of different economic entities which are self-interested in nature, system-wide goals are hard to achieve as they mostly collide with individual objectives and are therefore not intrinsically pursued (Parkes 2001).

Pricing Foundations and Implications on Web Service Pricing

One of the key building parts of a market is its market mechanism. This mechanism encloses the rules for allocation and pricing and thus regulates and, in some cases, enforces the procedures of trading on a specific market. In general, both issues of allocation and pricing are intrinsically tied to one another. Neumann et al. (2007) and Buyya et al. (2008) treat online trading platforms. For example, such as Grid and Cloud service exchanges. Yet, since allocation procedures are often directly connected to technical conditions, in the following the focus is mostly on the pricing mechanisms that are used in today’s markets. Basically, one can distinguish between static, flexible, and dynamic pricing, which can be further subdivided into concrete pricing schemes.

Web Services Advanced Reservation Contracts

In Chap. 5, the benefits which dynamic pricing mechanisms hold for providers as well as customers were pointed out. Though already commonly used in practice both market participants are faced with certain risks: Web service providers face the risk of low incomes in times of (unexpected) low utilization and demand, and are thus forced to lower their prices accordingly in order to keep up with their competitors. This may result in considerable losses. On the other hand, Web service customers may run into situations where, despite of a high willingness to pay, cannot fulfill their demand in peak times. While for the provider this dilemma has mainly direct economic consequences only, it can become a more serious issue to the customers than only high costs.