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

## TL;DR

This paper introduces the Temporal Clustering Algorithm (TCA), an incremental learning method for IoT devices that predicts usage patterns to optimize energy consumption and user comfort, achieving significant savings and high accuracy.

## Contribution

The paper presents a novel low-memory, configurable clustering algorithm for anticipatory IoT computing that balances user experience and energy efficiency.

## Key findings

- Energy savings up to 40% in water dispensers
- Over 90% accuracy in usage time prediction
- Low-cost implementation with less than 1Kbyte memory

## Abstract

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.