[go: up one dir, main page]

Candanedo et al., 2016 - Google Patents

Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models

Candanedo et al., 2016

View PDF
Document ID
10897810912251507499
Author
Candanedo L
Feldheim V
Publication year
Publication venue
Energy and buildings

External Links

Snippet

The accuracy of the prediction of occupancy in an office room using data from light, temperature, humidity and CO 2 sensors has been evaluated with different statistical classification models using the open source program R. Three data sets were used in this …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism

Similar Documents

Publication Publication Date Title
Candanedo et al. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models
Tekler et al. Occupancy prediction using deep learning approaches across multiple space types: A minimum sensing strategy
Saha et al. Occupancy sensing in buildings: A review of data analytics approaches
Huchuk et al. Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data
Wang et al. Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings
Wei et al. Vision-based detection and prediction of equipment heat gains in commercial office buildings using a deep learning method
Trivedi et al. Occupancy detection systems for indoor environments: A survey of approaches and methods
Yang et al. A systematic approach to occupancy modeling in ambient sensor-rich buildings
Cao et al. Smart sensing for HVAC control: Collaborative intelligence in optical and IR cameras
Raykov et al. Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction
Ryu et al. Development of an occupancy prediction model using indoor environmental data based on machine learning techniques
Alishahi et al. A framework to identify key occupancy indicators for optimizing building operation using WiFi connection count data
Tyndall et al. Occupancy estimation using a low-pixel count thermal imager
Van Kasteren et al. An activity monitoring system for elderly care using generative and discriminative models
Arief-Ang et al. Da-hoc: semi-supervised domain adaptation for room occupancy prediction using co2 sensor data
Yang et al. Inferring occupancy from opportunistically available sensor data
Mora et al. Occupancy patterns obtained by heuristic approaches: Cluster analysis and logical flowcharts. A case study in a university office
Abolhassani et al. Improving residential building energy simulations through occupancy data derived from commercial off-the-shelf Wi-Fi sensing technology
CN110136832B (en) Cognitive ability assessment system and method based on daily behaviors of old people
Yuan et al. Occupancy estimation in buildings based on infrared array sensors detection
Zhang et al. Room zonal location and activity intensity recognition model for residential occupant using passive-infrared sensors and machine learning
US20250181675A1 (en) Reducing false detections for night vision cameras
Arakawa Martins et al. Personal thermal comfort models: a deep learning approach for predicting older people’s thermal preference
Chiţu et al. Wireless system for occupancy modelling and prediction in smart buildings
Kim et al. Quantification of occupant response to influencing factors of window adjustment behavior using explainable AI