[go: up one dir, main page]

Schirmer et al., 2021 - Google Patents

Low-frequency energy disaggregation based on active and reactive power signatures

Schirmer et al., 2021

View PDF
Document ID
2952598387070399225
Author
Schirmer P
Mporas I
Publication year
Publication venue
2021 29th European Signal Processing Conference (EUSIPCO)

External Links

Snippet

Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power consumption as measured by a single smart meter at a household. Deep neural networks and especially Convolutional …
Continue reading at eurasip.org (PDF) (other versions)

Classifications

    • 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
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/141—Discrete Fourier transforms
    • 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
    • G06Q30/00—Commerce, e.g. shopping or e-commerce
    • G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202—Market predictions or demand forecasting
    • 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
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386—Retrieval requests
    • G06F17/30424—Query processing
    • G06F17/30533—Other types of queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks

Similar Documents

Publication Publication Date Title
Angelis et al. NILM applications: Literature review of learning approaches, recent developments and challenges
Hu et al. Review of model-based and data-driven approaches for leak detection and location in water distribution systems
Mao et al. A new online detection approach for rolling bearing incipient fault via self-adaptive deep feature matching
Bonfigli et al. Denoising autoencoders for non-intrusive load monitoring: improvements and comparative evaluation
Xia et al. Non-intrusive load disaggregation based on deep dilated residual network
Wang et al. An adaptive daily runoff forecast model using VMD-LSTM-PSO hybrid approach
CN107122790B (en) Non-invasive load identification algorithm based on hybrid neural network and ensemble learning
Lin et al. Non-intrusive residential electricity load decomposition via low-resource model transferring
CN107677472B (en) Bearing state noise diagnosis algorithm for network variable selection and feature entropy fusion
Antonopoulos et al. Data-driven modelling of energy demand response behaviour based on a large-scale residential trial
Schirmer et al. Low-frequency energy disaggregation based on active and reactive power signatures
Liu et al. SAMNet: Toward latency-free non-intrusive load monitoring via multi-task deep learning
Zhang et al. Support vector regression based on grid‐search method for short‐term wind power forecasting
Vitter et al. A non-intrusive approach for classifying residential water events using coincident electricity data
Wu et al. Event‐based non‐intrusive load identification algorithm for residential loads combined with underdetermined decomposition and characteristic filtering
CN103996071A (en) Wind power plant wind speed prediction method based on Markov theory
Yadav et al. NILM based energy disaggregation algorithm for dairy farms
Precioso et al. NILM as a regression versus classification problem: the importance of thresholding
CN111898644A (en) An intelligent identification method for aerospace liquid engine health status under fault-free samples
Schirmer et al. Double Fourier integral analysis based convolutional neural network regression for high-frequency energy disaggregation
He et al. InFocus: Amplifying critical feature influence on non-intrusive load monitoring through self-attention mechanisms
Ruhang Efficient clustering for aggregate loads: An unsupervised pretraining based method
Schirmer et al. Energy disaggregation from low sampling frequency measurements using multi-layer zero crossing rate
Santos et al. An energy disaggregation approach based on deep neural network and wavelet transform
Liu et al. A single-to-multi network for latency-free non-intrusive load monitoring