Noname manuscript No. (will be inserted by the editor) Embedding Intelligent Eco-aware Systems within Everyday Things to Increase People’s Energy Awareness

Abstract There is a lack of energy consumption aware-ness in working spaces. People in their workplaces do not receive energy consumption feedback nor do they pay a monthly invoice to electricity providers. In order to enhance workers ’ energy awareness, we have trans-formed everyday shared electrical appliances which are placed in common spaces (e.g. beamer projectors, coffee-makers, printers, screens, portable fans, kettles, and so on.) into persuasive eco-aware everyday things. The proposed approach lets these appliances report their us-age patterns to a Cloud-server where the data is trans-formed into time-series and then processed to obtain the appliances ’ next-week usage forecast. Autoregres-sive Integrated Moving Average (ARIMA) model has been selected as the potentially most accurate method for processing such usage predictions when compared with the performance exhibited by three different con-figurations of Artificial Neural Networks (ANNs). Our major contribution is the application of soft computing techniques to the field of sustainable persuasive tech-nologies. Thus, consumption predictions are used to trigger timely persuasive interactions to help devices users to operate the appliances as efficiently, energy-wise, as possible. Qualitative and quantitative results were gathered in a between-three-groups study related with the use of shared electrical coffee-makers at work