Architectural Engineering and Construction, Durham School of

 

Date of this Version

2018

Citation

International High Performance Buildings Conference. Paper 247.

Comments

5th International High Performance Buildings Conference at Purdue, July 9-12, 2018

Abstract

This paper investigates a mechanism in which indoor air temperature can be predicted using the temperature sensor on the battery and the CPU of smartphones or laptops, where data is easily accessible and ubiquitous. As a case study, several machine-learning methods were used to build models from a laptop data and the measured surrounding air temperature. The effects of the machine learning type and input feature size (by including other parameters such as CPU processing usage and battery charge percentage) on the model accuracy were investigated. The goal is to determine a set of feature combinations that can be used to build models which can accurately predict the indoor temperature. The accuracy of these models was measured by comparing their prediction to the actual indoor temperature.

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