Affiliations: Deusto Institute of Technology – Deusto Tech,
University of Deusto, Bilbao, Spain | University of Deusto, Bilbao, Spain
Note: [] Corresponding author: Diego Casado-Mansilla, Deusto Institute of
Technology, University of Deusto, Avda. Universidades 24, 48007, Bilbao, Spain.
E-mail: dcasado@deusto.es
Abstract: Society wastes much more energy than it should. This produces tons
of unnecessary CO_2 emissions. This is partly due to the
inadequate use of electrical devices given the intangible and invisible nature
of energy. This misuse of devices and energy unawareness is particularly
relevant in public spaces (offices, schools, hospitals and so on), where people
use electrical appliances, but they do not directly pay the invoice to energy
providers. Embedding intelligence within public, shared appliances,
transforming them into Eco-aware things, is valuable to reduce a proportion of
the unnecessarily consumed energy. To this end, we present a twofold approach
for better energy efficiency in public spaces: 1) informing persuasively to
concerned users about the misuse of electronic appliances; 2) Customizing the
operating mode of this everyday electrical appliances as a function of their
real usage pattern. To back this approach, a capsule-based coffee machine
placed in a research laboratory has been augmented. This device is able to
continuously collect its usage pattern to offer feedback to coffee consumers
about the energy wasting and also, to intelligently adapt its operation to
reduce wasted energy. To this aim, several machine learning approaches are
compared and evaluated to forecast the next-day device usage.
