Explainable AI for Tailored Electricity Consumption Feedback : An Experimental Evaluation of Visualizations
Faculty/Professorship: | Information Systems and Energy Efficient Systems |
Author(s): | Wastensteiner, Jacqueline; Weiss, Tobias M.; Haag, Felix ; Hopf, Konstantin ![]() |
Corporate Body: | Ali El Quammah, HEM Business School, Morocco |
Conference: | 29. European Conference on Information Systems (ECIS), Marrakesh: Morocco |
Publisher Information: | Association for Information Systems (AIS) |
Year of publication: | 2021 |
Pages: | 1-19 |
ISBN: | 978-1-7336325-6-0 |
Language(s): | English |
Abstract: | Machine learning (ML) methods can effectively analyse data, recognize patterns in them, and make high-quality predictions. Good predictions usually come along with “black-box” models that are unable to present the detected patterns in a human-readable way. Technical developments recently led to eXplainable Artificial Intelligence (XAI) techniques that aim to open such black-boxes and enable humans to gain new insights from detected patterns. We investigated the application of XAI in an area where specific insights can have a significant effect on consumer behaviour, namely electricity use. Knowing that specific feedback on individuals’ electricity consumption triggers resource conservation, we created five visualizations with ML and XAI methods from electricity consumption time series for highly personalized feedback, considering existing domain-specific design knowledge. Our experimental evaluation with 152 participants showed that humans can assimilate the pattern displayed by XAI visualizations, but such visualizations should follow known visualization patterns to be well-understood by users. |
GND Keywords: | Intelligenter Zähler; Künstliche Intelligenz; Maschinelles Lernen; Datenvisualisierung; Energieeinsparung; Rückmeldung |
Keywords: | eXplainable Artificial Intelligence, Visualizations, Energy Conservation, Machine Learning, Feedback. |
DDC Classification: | 004 Computer science 330 Economics |
RVK Classification: | ST 530 |
Type: | Conferenceobject |
URI: | https://fis.uni-bamberg.de/handle/uniba/53989 |
Release Date: | 10. May 2022 |
Project: | Kombinierte Verhaltens- und Analyse-Innovation zur Steigerung der Energieeffizienz mittels Smart Meter in Privathaushalt; Teilprojekt: Maschinelle Lernverfahren für Energieeffizienz-Feedback |

originated at the
University of Bamberg
University of Bamberg