Using Deep Learning Methods to Monitor Non-Observable States in a Building

Abstract

This paper presents results from ongoing research with a goal to use a combination of time series from non-intrusive soft sensors and deep recurrent neural networks to predict room usage at a university campus. Training data was created by collecting measurements from sensors measuring room CO2, humidity, temperature, light, motion and sound, while the labels was created manually by human inspection. Results include analyses of relationships between different sensor data sequences and recommendations for a prototype predictive model using deep recurrent neural networks.

Intelligent Process Automation of Industries Using Artificial Intelligence and Machine Learning

“…The establishment of ML is because of the research of computer learning theory and recognition pattern. Algorithmic application of ML happens with the unsupervised and supervised learning as shown in Fig [6] have presented the theories showing that methods of Deep Learning (DL) with Neural Networks (NN) are always considered when AI is mentioned. The rapid enhanced progress of DL over the decades can significantly lead to powerful hardware used in computing activities.…”