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We present here the Temporal Clustering Algorithm (TCA), an incrementallearning algorithm applicable to problems of anticipatory computing in thecontext of the Internet of Things. This algorithm was tested in a specificprediction scenario of consumption of an electric water dispenser typicallyused in tropical countries, in which the ambient temperature is around30-degree Celsius. In this context, the user typically wants to drinking icedwater therefore uses the cooler function of the dispenser. Real and syntheticwater consumption data was used to test a forecasting capacity on how muchenergy can be saved by predicting the pattern of use of the equipment. Inaddition to using a small constant amount of memory, which allows the algorithmto be implemented at the lowest cost, while using microcontrollers with a smallamount of memory (less than 1Kbyte) available on the market. The algorithm canalso be configured according to user preference, prioritizing comfort, keepingthe water at the desired temperature longer, or prioritizing energy savings.The main result is that the TCA achieved energy savings of up to 40% comparedto the conventional mode of operation of the dispenser with an average successrate higher than 90% in its times of use.

A Temporal Clustering Algorithm for Achieving the trade-off between the User Experience and the Equipment Economy in the Context of IoT