A Cognitive Computing Solution to Foster Retailing of Renewable Energy Systems





Faculty/Professorship: Information Systems and Energy Efficient Systems  
Author(s): Weigert, Andreas  ; Hopf, Konstantin  ; Staake, Thorsten
Corporate Body: SIGGreen Pre-ICIS Workshop, 2019, Munich
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2020
Pages: 3
Source/Other editions: HCI/MIS Workshop 2019: The 18th Annual Pre-ICIS Workshop on HCI Research in MIS Sponsored by AIS SIGHCI, 2019, München
is version of: 10.20378/IRB-47040
Year of first publication: 2019
Language(s): English
DOI: 10.20378/irb-47040
Licence: Creative Commons - CC BY-SA - Attribution - ShareAlike 4.0 International 
URN: urn:nbn:de:bvb:473-irb-470409
Abstract: 
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.
GND Keywords: Erneuerbare Energien; Vertrieb; Informationssystem; Künstliche Intelligenz; Maschinelles Lernen
Keywords: Renewable energy systems, sales support system, cognitive computing, machine learning
DDC Classification: 004 Computer science  
333.7 Natural ressources, energy, environment  
RVK Classification: ST 300   
Peer Reviewed: Ja
International Distribution: Ja
Type: Conferenceobject
URI: https://fis.uni-bamberg.de/handle/uniba/47040
Release Date: 2. June 2020

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