Reducing Energy Consumption by Behavioural Change

Dietrich Albert*, Michael Bedek, Wolfgang Horn

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


On global as well as national levels, top-down approaches to reduce climate-related energy consumption have not been successful and effective at all until now. Thus, a bottom-up approach is recommended: citizens role should change from consumers to prosumers, to (co-)decision-makers regarding energy-related behaviour, and to co-investors in the new energy infrastructure. However behavioural change is difficult to realize in face of the ‘knowledge-behaviour gap’, ‘value-action gap’ or ‘attitude-behaviour gap’. Thus, the CODALoop project collected data with its platform Energanz and developed the socio-cognitive model for energy-related behaviour. In the new planned IF4E project that model becomes an integrated module of a process model for energy-related behavioural change with real-time feedback loops, real-time energy consumption data, and a user’s Personal Energy Profile (PEP) which is updated continuously. The challenges and risks in investigating and implementing the proposed process model in real settings for changing climate-related energy consumption behaviour are discussed. This position paper aims receiving feedback of experts regarding the planned project before its realization.

Original languageEnglish
Title of host publicationActivity and Behavior Computing
EditorsMd Atiqur Rahman Ahad, Sozo Inoue, Daniel Roggen, Kaori Fujinami
PublisherSpringer Nature
Number of pages12
ISBN (Print)9789811589430
Publication statusPublished - 2021
Event2020 International Conference on Activity and Behavior Computing - Kitakyushu, Japan
Duration: 26 Aug 202029 Aug 2020

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026


Conference2020 International Conference on Activity and Behavior Computing
Abbreviated titleABC2020

ASJC Scopus subject areas

  • Computer Science(all)
  • Decision Sciences(all)


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