1 research outputs found
A Temporal Clustering Algorithm for Achieving the trade-off between the User Experience and the Equipment Economy in the Context of IoT
We present here the Temporal Clustering Algorithm (TCA), an incremental
learning algorithm applicable to problems of anticipatory computing in the
context of the Internet of Things. This algorithm was tested in a specific
prediction scenario of consumption of an electric water dispenser typically
used in tropical countries, in which the ambient temperature is around
30-degree Celsius. In this context, the user typically wants to drinking iced
water therefore uses the cooler function of the dispenser. Real and synthetic
water consumption data was used to test a forecasting capacity on how much
energy can be saved by predicting the pattern of use of the equipment. In
addition to using a small constant amount of memory, which allows the algorithm
to be implemented at the lowest cost, while using microcontrollers with a small
amount of memory (less than 1Kbyte) available on the market. The algorithm can
also be configured according to user preference, prioritizing comfort, keeping
the water at the desired temperature longer, or prioritizing energy savings.
The main result is that the TCA achieved energy savings of up to 40% compared
to the conventional mode of operation of the dispenser with an average success
rate higher than 90% in its times of use.Comment: 9 pages, 2 figure