Predictive Analytics for Energy Efficiency and Energy Retailing

  • Digitization causes large amounts of data in organizations (e.g., transaction data from business processes, communication data, sensor data). Besides, a large number of data sources are emerging online and can be freely used. Firms are looking for ways to commercialize this increasing amount of data and research aims to better understand the data value creation process. The present dissertation answers five central research questions in this context and examines how machine learning (ML) can be used to create value from data, using case studies from energy retailing and energy efficiency. First, a systematic literature review gives an overview of firm internal and external data sources for potential analyses. Second, the importance of human cognition, theory, and expert knowledge in effective data preparation for ML is demonstrated. Third, current ML algorithms and variable selection methods are empirically compared using industry data sets. Implications for theory and practice are identified. Finally, the successful use of theDigitization causes large amounts of data in organizations (e.g., transaction data from business processes, communication data, sensor data). Besides, a large number of data sources are emerging online and can be freely used. Firms are looking for ways to commercialize this increasing amount of data and research aims to better understand the data value creation process. The present dissertation answers five central research questions in this context and examines how machine learning (ML) can be used to create value from data, using case studies from energy retailing and energy efficiency. First, a systematic literature review gives an overview of firm internal and external data sources for potential analyses. Second, the importance of human cognition, theory, and expert knowledge in effective data preparation for ML is demonstrated. Third, current ML algorithms and variable selection methods are empirically compared using industry data sets. Implications for theory and practice are identified. Finally, the successful use of the information gained through ML is exemplified through case studies where increased energy efficiency, customer value, and service quality can demonstrate economic, environmental, and social value. Thus, this empirical work contributes to the so far rather conceptual discussion on value creation from big data in information systems research.show moreshow less

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Metadaten
Institutes:Fakultät Wirtschaftsinformatik und Angewandte Informatik / Fakultät Wirtschaftsinformatik und Angewandte Informatik: Abschlussarbeiten
Fakultät Wirtschaftsinformatik und Angewandte Informatik / Lehrstuhl für Wirtschaftsinformatik, insbesondere Energieeffiziente Systeme
Author:Konstantin HopfORCiD
Advisor:Thorsten Staake
Place of publication:Bamberg
Publisher:University of Bamberg Press
Year of publication:2019
Pages / Size:XXVI, 251 Seiten : Illustrationen, Diagramme
Collections (Serial Number):Schriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich-Universität Bamberg (36)
Remarks:Dissertation, Otto-Friedrich-Universität Bamberg, 2019
Source/Other editions:Parallel erschienen als Druckausg. in der University of Bamberg Press, 2019 (32,50 EUR)
To order a print copy: http://www.uni-bamberg.de/ubp/
SWD-Keyword:Maschinelles Lernen ; Prognose ; Statistisches Verfahren ; Energie
Keywords:Ambient Data; Data Value Creation Process; Energy Retailing; Machine Learning; Predictive Analytics
DDC-Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
RVK-Classification:ST 515
URN:urn:nbn:de:bvb:473-opus4-548335
DOI:https://doi.org/10.20378/irbo-54833
ISBN:978-3-86309-668-7
ISBN:978-3-86309-669-4
Document Type:Dissertation
Language:English
Publishing Institution:University of Bamberg Press, Universitätsbibliothek Bamberg
Release Date:2019/07/18
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International