Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space
Fergianto E. Gunawan, Arief Suriadi Budiman, Bens Pardamean, Endang Juana, Sugiarto Romeli, Tjeng Wawan Cenggoro, Kartika Purwandari, Alam A. Hidayat, Anak Agung Ngurah Perwira Redi, Muhammad Asrol
Abstract
An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.