# IoFClime: The fuzzy logic and the Internet of Things to control indoor   temperature regarding the outdoor ambient conditions

**Authors:** Daniel Meana-Llori\'an, Cristian Gonz\'alez Garc\'ia, B. Cristina, Pelayo G-Bustelo, Juan Manuel Cueva Lovelle, Nestor Garcia-Fernandez

arXiv: 1701.02545 · 2017-01-11

## TL;DR

This paper proposes a fuzzy logic-based system integrated with IoT platforms to optimize indoor temperature control by considering outdoor conditions, resulting in significant energy savings and improved comfort.

## Contribution

It introduces a novel fuzzy logic approach for IoT-enabled indoor temperature regulation that accounts for outdoor environmental factors, enhancing energy efficiency.

## Key findings

- Achieves around 40% energy savings.
- Effectively manages indoor temperature based on outdoor conditions.
- Demonstrates the feasibility of fuzzy logic in IoT temperature control systems.

## Abstract

The Internet of Things is arriving to our homes or cities through fields already known like Smart Homes, Smart Cities, or Smart Towns. The monitoring of environmental conditions of cities can help to adapt the indoor locations of the cities in order to be more comfortable for people who stay there. A way to improve the indoor conditions is an efficient temperature control, however, it depends on many factors like the different combinations of outdoor temperature and humidity. Therefore, adjusting the indoor temperature is not setting a value according to other value. There are many more factors to take into consideration, hence the traditional logic based in binary states cannot be used. Many problems cannot be solved with a set of binary solutions and we need a new way of development. Fuzzy logic is able to interpret many states, more than two states, giving to computers the capacity to react in a similar way to people. In this paper we will propose a new approach to control the temperature using the Internet of Things together its platforms and fuzzy logic regarding not only the indoor temperature but also the outdoor temperature and humidity in order to save energy and to set a more comfortable environment for their users. Finally, we will conclude that the fuzzy approach allows us to achieve an energy saving around 40% and thus, save money.

## Full text

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## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/1701.02545/full.md

## References

39 references — full list in the complete paper: https://tomesphere.com/paper/1701.02545/full.md

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Source: https://tomesphere.com/paper/1701.02545