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

Zhang et al., 2011 - Google Patents

Anomaly detection in premise energy consumption data

Zhang et al., 2011

Document ID
668263514292186286
Author
Zhang Y
Chen W
Black J
Publication year
Publication venue
2011 IEEE power and energy society general meeting

External Links

Snippet

Demand Response (DR) programs are designed to reduce energy consumption for relatively short time periods (eg, a few hours per event). It has been widely recognized that DR can help to meet both reliability and market needs. In order for DR programs to achieve …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • 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
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G06Q10/0639—Performance analysis
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • G06Q30/0201—Market data gathering, market analysis or market modelling
    • 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
    • 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
    • G06Q50/06—Electricity, gas or water supply

Similar Documents

Publication Publication Date Title
Zhang et al. Anomaly detection in premise energy consumption data
Zhao et al. Non-intrusive load disaggregation solutions for very low-rate smart meter data
Wang et al. Review of smart meter data analytics: Applications, methodologies, and challenges
Faustine et al. A survey on non-intrusive load monitoring methodies and techniques for energy disaggregation problem
US8412491B2 (en) System and method for electric patterns discovery
Cominola et al. A hybrid signature-based iterative disaggregation algorithm for non-intrusive load monitoring
Funde et al. Motif-based association rule mining and clustering technique for determining energy usage patterns for smart meter data
Gaur et al. Performance evaluation of techniques for identifying abnormal energy consumption in buildings
Liang et al. A data-driven approach for targeting residential customers for energy efficiency programs
US9576245B2 (en) Identifying electric vehicle owners
Villar-Rodriguez et al. Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis
Ploennigs et al. Exploiting generalized additive models for diagnosing abnormal energy use in buildings
Liang et al. HVAC load disaggregation using low-resolution smart meter data
Najafi et al. Building characterization through smart meter data analytics: Determination of the most influential temporal and importance-in-prediction based features
Batra et al. Gemello: Creating a detailed energy breakdown from just the monthly electricity bill
Chen et al. Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms
Batra et al. Neighbourhood nilm: A big-data approach to household energy disaggregation
Verma et al. Analyzing household charging patterns of Plug-in electric vehicles (PEVs): A data mining approach
US9952061B2 (en) Detecting fraud in resource distribution systems
Amara korba et al. Smart grid energy fraud detection using SVM
Hartmann et al. Suspicious electric consumption detection based on multi-profiling using live machine learning
Saad et al. Data analytics-based anomaly detection in smart distribution network
Kawoosa et al. Using machine learning ensemble method for detection of energy theft in smart meters
Sodenkamp et al. Smart meter data analytics for enhanced energy efficiency in the residential sector
Ramadan et al. Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO