Using Deep Learning Methods to Monitor Non-Observable States in a Building
Copyright policy )Using Deep Learning Methods to Monitor Non-Observable States in a Building
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.
Indoor Air Quality, Occupancy Prediction, PCA, LSTM, GRU, Neural Architecture Search, Deep Learning, Internet of Things, VDP::Mathematics and natural science: 400::Information and communication science: 420::Knowledge based systems: 425, VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kunnskapsbaserte systemer: 425, VDP::Technology: 500::Information and communication technology: 550, VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550
Indoor Air Quality, Occupancy Prediction, PCA, LSTM, GRU, Neural Architecture Search, Deep Learning, Internet of Things, VDP::Mathematics and natural science: 400::Information and communication science: 420::Knowledge based systems: 425, VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kunnskapsbaserte systemer: 425, VDP::Technology: 500::Information and communication technology: 550, VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550
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