Kundenportale in der Energiebranche: Bestandsaufnahme und Entwicklungspotenziale

Zusammenfassung: Seit der Liberalisierung des Energiemarktes hat der Wettbewerb unter den Energieversorgern (EVU) zugenommen. Um neue Kunden zu gewinnen und bestehende zu halten, müssen diese daher neue Services und Produkte entwickeln, die den Bedürfnissen der Kunden in hohem Masse entsprechen. Ein Online-Kundenportal kann einen solchen Service darstellen, da es zukünftig als zentrale Schnittstelle zum Kunden fungieren wird, wenn der persönliche Kontakt aufgrund eines abnehmenden regionalen Fokus der EVU und dem Wegfall des Zähler-Ablesens vor Ort durch Smart Metering abnimmt. In diesem Beitrag werden die Kriterien, die für die Gestaltung eines solches Portals relevant sind, vorgestellt. Eine Marktübersicht zeigt auf, welche dieser Kriterien bereits umgesetzt werden und wo Verbesserungspotenzials bestehen. Ein spezieller Fokus liegt auf psychologische Konzepte, die den Kunden motivieren, das Portal einerseits regelmässig zu nutzen und andererseits Energie zu sparen. Es zeigt sich, dass gerade in Bezug auf Kriterien zur Veranschaulichung des Energieverbrauches sowie bei der Ausgestaltung von psychologischen Anreizen ein enormes Verbesserungspotenzial vorhanden is

A Decision Support System for Photovoltaic Potential Estimation

With knowledge on the photovoltaic potential of individual residential buildings, solar companies, energy service providers and electric utilities can identify suitable customers for new PV installations and directly address them in renewable energy rollout and maintenance campaigns. However, many currently used solutions for the simulation of energy generation require detailed information about houses (roof tilt, shading, etc.) that is usually not available at scale. On the other hand, the methodologies enabling extraction of such details require costly remote-sensing data from three-dimensional (3D) laser scanners or aerial images. To bridge this gap, we present a decision support system (DSS) that estimates the potential amount of electric energy that could be generated at a given location if a photovoltaic system would be installed. The DSS automatically generates insights about photovoltaic yields of individual roofs by analyzing freely available data sources, including the crowdsourced volunteered geospatial information systems OpenStreetMap and climate databases. The resulting estimates pose a valuable foundation for selecting the most prospective households (e.g., for personal visit and screening by an expert) and targeted solar panel kit offerings, ultimately leading to significant reduction of manual human efforts, and to cost-effective personalized renewables adoption

PowerPedia: changing energy usage with the help of a community-based smartphone application

When it comes to conserving electricity, it is crucial for users to know how much energy is consumed by individual appliances. However, the technical feedback provided by existing energy consumption feedback systems in the form of dry numbers and intangible units is not appropriate for most users. To address this shortcoming, we developed PowerPedia, a system that provides behavior-influencing feedback over and above pure consumption values. By integrating a community platform—a Wikipedia for electrical appliances—PowerPedia enables users to identify and compare the consumption of their domestic appliances with that of others. It thus helps users to better understand their electricity consumption and take effective action to save electricit

The value of RFID for RTI management

Returnable transport items (RTIs) are key elements for enabling a smooth flow of goods throughout supply chains. Despite their importance, RTIs can be prone to high loss and breakage rates. Today's RTI management processes are rather inefficient and are based on estimates about when, where and how RTIs are utilised. This limited visibility inevitably causes the involved parties to feel less responsible for the proper management of RTIs. As a consequence, inefficiencies created by a single party can result in a significant cost burden for the whole supply chain. The goal of this paper is therefore to explore the impact of increased asset visibility on the RTI management process. We describe a solution based on Radio Frequency Identification (RFID) technology and quantify its financial impact from each individual stakeholder's perspective. Our findings suggest that RFID can provide a powerful means to counter inefficiencies in the RTI management process and improve the overall effectiveness of the RTI supply chain networ

Information Systems Research for the Next Generation: Child-Centricity in a Digital World

Traditionally, information systems (IS) research investigates socio-technical systems in organizations and the workplace. As IS have become an integral part of our daily lives, IS research nowadays also incorporates the private space. However, efforts to date have mostly focused on adults. Children, born into a digital world today, have been mostly left out. Yet our discipline not only has the potential to contribute to the adequate and child-friendly design of IS artifacts for children but can also help to further develop theories on children's behavior. For this to succeed, IS researchers need to adapt their approach to children. Ethical considerations should address children's vulnerability, the design of interventions should happen in close collaboration with children, research methods should be child-centered, and the specificities of children should be kept present in result analyses

Resolving the Misalignment between Consumer Privacy Concerns and Ubiquitous IS Design: The Case of Usage-based Insurance

Ubiquitous IS enables novel services and business models, yet require a careful balancing of consumer privacy concerns (PC) – induced by the provision of particular sensors and information types – with functional performance in order to maximize acceptance. For the exemplary case of Usage-based Insurance (UBI), this paper presents a design science approach to the mitigation of PC under parallel consideration of functional system performance. Based on long-term location trajectories from 1’600 vehicles, we assess the predictive power of emulated system designs that substitute location information, presumably the most privacy sensitive type of information in current UBI designs. We find that there are substantial grounds to challenge prevalent design paradigms in UBI and infer general insights from this example for IS researchers and IT professionals, who, when seeking to improve system privacy, often focus on privacy-enhancing technologies instead of considering the socio-technical context of ubiquitous IS

Value creation from analytics with limited data : a case study on the retailing of durable consumer goods

Companies are pinning high hopes on competitive advantages through data analytics. So far, value gains through analytics have been demonstrated for IT-heavy and data-rich business areas. Yet, research has paid little attention to value creation through data analytics in the plethora of companies with limited data (i.e., having transactions in the hundreds and attributes in the tens). Building on the literature of big data value creation and the resource-based view, we carried out an in-depth analytics case study with a retailer of renewable energy systems. Firms in this business area operate with expensive but few sales, so their available data are notoriously limited. Our findings demonstrate that data analytics capabilities and value creation mechanisms (democratize, contextualize, experiment with data, and execute data insights) are also effective in situations with limited data. Practice and research should therefore put not only emphasis on the volume and the variety of data but also on contextual factors related to managers (e.g., clear strategy, vision, leadership) and all employees (e.g., openness for agile working mode, data awareness)

Supervised classification with interdependent variables to support targeted energy efficiency measures in the residential sector

This paper presents a supervised classification model, where the indicators of correlation between dependent and independent variables within each class are utilized for a transformation of the large-scale input data to a lower dimension without loss of recognition relevant information. In the case study, we use the consumption data recorded by smart electricity meters of 4200 Irish dwellings along with half-hourly outdoor temperature to derive 12 household properties (such as type of heating, floor area, age of house, number of inhabitants, etc.). Survey data containing characteristics of 3500 households enables algorithm training. The results show that the presented model outperforms ordinary classifiers with regard to the accuracy and temporal characteristics. The model allows incorporating any kind of data affecting energy consumption time series, or in a more general case, the data affecting class-dependent variable, while minimizing the risk of the curse of dimensionality. The gained information on household characteristics renders targeted energy-efficiency measures of utility companies and public bodies possible

Explaining and predicting annual electricity demand of enterprises – a case study from Switzerland

In an attempt to channel sales activities, companies often focus on ‘high value targets’ that offer attractive prospective returns. In liberalized electricity markets, commercial customers with high electricity demand constitute such high value targets. The problem when acquiring new customers, however, is that the electricity consumption is not known to the sales organization in advance. This hinders the possibility to prioritize sales targets and thus increases the acquisition cost, reduces the competitiveness within the market and ultimately leads to higher cost for electricity customers. In this study, we investigate the annual electricity consumption of enterprises by means of a dataset with 1810 company addresses in a typical town in Switzerland. We use the industry branch of the enterprises together with open big data (geographic information, online-content, social media data and governmental statistical data) to explain and predict the electricity consumption of such. Our linear regression analysis shows that information on the economic branches of the enterprises, basal area of buildings, number of opening hours and social media data can explain up to 19% of variance in electricity consumption. Economic trends (e.g., in labor market and turnover statistics) reflect changes in the electricity consumption in the investigated years 2010–2014 for several economic branches. We show, that the electricity consumption can be predicted better than a random predictor, however with a high uncertainty. Nevertheless, the open data sources can be used to identify a relevant group of companies with high consumption (more than 100,000kWh per year) with good accuracy

A Cognitive Computing Solution to Foster Retailing of Renewable Energy Systems

Renewable energy systems (RES) in the residential sector, like photovoltaic systems, heat pumps and battery storage, are corner¬stones of a sustainable energy supply. Nevertheless–and despite major fiscal stimuli–private investment in such technologies has not yet reached a satisfactory level, also because sale of such products is time-consuming and requires a high level of expertise from suppliers. In practice, small and medium-sized installation firms are often responsible for addressing customers, advising, designing and implementing the appropriate systems, but they struggle with offering the complex technology and are exposed to fierce competition in their market. In a joint research initiative with a RES supplier and a software development company, we drive the development of information systems that support installation companies in their tasks. To this end, we are using action design to develop a cognitive computing solution based on Machine Learning (ML) to promote the sale of sustainable energy products. Based on 4,909 real customer requests for RES and survey data from 666 homeowners (which we use as ground truth data for ML), a predictive model can reliably identify promising RES installations out of a list of customer requests and thereby supports an important business task. Despite these promising results, we face a number of challenges in developing our cognitive computing solution. To address these challenges, design principles for similar systems are developed, contributing to the current debate on how information systems research can support sustainable development and how artificial intelligence can be used profitably in enterprises