Nothing Special   »   [go: up one dir, main page]

Kong et al., 2016 - Google Patents

Improving nonintrusive load monitoring efficiency via a hybrid programing method

Kong et al., 2016

Document ID
6126720860162627051
Author
Kong W
Dong Z
Hill D
Luo F
Xu Y
Publication year
Publication venue
IEEE Transactions on Industrial Informatics

External Links

Snippet

Nonintrusive load monitoring (NILM) aims to disaggregate the total power consumption profile measured at the household power inlet into device-level insights. While many studies focus on the modeling methodologies, few of them address the challenge of the computation …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202Market predictions or demand forecasting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/06Electricity, gas or water supply
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Kong et al. Improving nonintrusive load monitoring efficiency via a hybrid programing method
Kong et al. An extensible approach for non-intrusive load disaggregation with smart meter data
Kong et al. A practical solution for non-intrusive type II load monitoring based on deep learning and post-processing
Kong et al. A hierarchical hidden Markov model framework for home appliance modeling
Kaselimi et al. Multi-channel recurrent convolutional neural networks for energy disaggregation
Egarter et al. PALDi: Online load disaggregation via particle filtering
Welikala et al. Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting
Cui et al. Estimation of target appliance electricity consumption using background filtering
Parson et al. An unsupervised training method for non-intrusive appliance load monitoring
Fang et al. Nonintrusive appliance identification with appliance-specific networks
Zhai et al. Appliance flexibility analysis considering user behavior in home energy management system using smart plugs
Azizi et al. Residential household non-intrusive load monitoring via smart event-based optimization
Machlev et al. Modified cross-entropy method for classification of events in NILM systems
Giri et al. An energy estimation framework for event-based methods in non-intrusive load monitoring
Liu et al. Real-time corporate carbon footprint estimation methodology based on appliance identification
Jia et al. A fully unsupervised non-intrusive load monitoring framework
Jiang et al. A physical probabilistic network model for distribution network topology recognition using smart meter data
Ali et al. Households electricity consumption analysis with data mining techniques
Tang et al. A distributed and scalable approach to semi-intrusive load monitoring
Raiker et al. Energy disaggregation using energy demand model and IoT-based control
Kumar et al. A time efficient factorial hidden Markov model-based approach for non-intrusive load monitoring
Ghorbanpour et al. Swarm and evolutionary algorithms for energy disaggregation: Challenges and prospects
Dinesh et al. Residential power forecasting based on affinity aggregation spectral clustering
Wang et al. A factorial hidden markov model for energy disaggregation based on human behavior analysis
Ramchurn et al. Agentswitch: Towards smart energy tariff selection