Development of an occupancy prediction model using indoor environmental data based on machine learning techniques

Building and Environment

Volumn 107, Issue , 2016, Pages 1-9

  Ryu, Seung Ho ^a,b   Moon, Hyeun Jun ^b  

^a KT Cooperation   (South Korea)

^b Dankook University   (South Korea)

Author keywords

Decision tree model; Hidden Markov model; Indoor environmental data; Occupancy prediction model; Occupant behavior

Indexed keywords

ARTIFICIAL INTELLIGENCE; CARBON DIOXIDE; DATA MINING; DECISION TREES; ENERGY UTILIZATION; FORECASTING; LEARNING ALGORITHMS; LEARNING SYSTEMS; LIGHTING; MARKOV PROCESSES; TREES (MATHEMATICS);

BUILDING CONTROLS; BUILDING INTEGRATED; DECISION TREE MODELING; ENVIRONMENTAL DATA; MACHINE LEARNING TECHNIQUES; OCCUPANCY DETECTIONS; OCCUPANCY PREDICTIONS; OCCUPANT BEHAVIOR;

HIDDEN MARKOV MODELS;

ALGORITHM; CARBON DIOXIDE; INDOOR AIR; LEARNING; MARKOV CHAIN; MODELING;

EID: 84978776062     PISSN: 03601323     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.buildenv.2016.06.039     Document Type: Article

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