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

Ouyang et al., 2017 - Google Patents

A combined multivariate model for wind power prediction

Ouyang et al., 2017

Document ID
4734230286008564890
Author
Ouyang T
Zha X
Qin L
Publication year
Publication venue
Energy Conversion and Management

External Links

Snippet

The intermittent and fluctuation of wind power has a harmful effect on power grid. To direct system operators to mitigate the harm, a combined multivariate model is proposed to improve wind power prediction accuracy. This model is built through two stages. First, valid …
Continue reading at www.sciencedirect.com (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
    • G06Q10/0639Performance analysis
    • G06Q10/06393Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/18Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • 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
    • 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
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00Systems controlled by a computer
    • G05B15/02Systems controlled by a computer electric
    • 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
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Ouyang et al. A combined multivariate model for wind power prediction
Yuan et al. Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine
Kazemzadeh et al. A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting
Yan et al. Advanced wind power prediction based on data-driven error correction
Osório et al. Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information
Liu et al. A recursive ensemble model for forecasting the power output of photovoltaic systems
Günay Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey
Prema et al. Development of statistical time series models for solar power prediction
Dong et al. Wind power day-ahead prediction with cluster analysis of NWP
Tascikaraoglu et al. A review of combined approaches for prediction of short-term wind speed and power
Kani et al. Very short-term wind speed prediction: A new artificial neural network–Markov chain model
Bilgili et al. Electric energy demands of Turkey in residential and industrial sectors
Nguyen et al. A new method for forecasting energy output of a large-scale solar power plant based on long short-term memory networks a case study in Vietnam
Li et al. Applications of Bayesian methods in wind energy conversion systems
Wang et al. A hybrid forecasting model based on outlier detection and fuzzy time series–A case study on Hainan wind farm of China
US20150317589A1 (en) Forecasting system using machine learning and ensemble methods
Sengar et al. Ensemble approach for short term load forecasting in wind energy system using hybrid algorithm
Sareen et al. An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction
Carapellucci et al. A methodology for the synthetic generation of hourly wind speed time series based on some known aggregate input data
Abdel-Basset et al. STLF-Net: Two-stream deep network for short-term load forecasting in residential buildings
Kaur et al. Ensemble re-forecasting methods for enhanced power load prediction
Quiñones et al. Towards smart energy management for community microgrids: Leveraging deep learning in probabilistic forecasting of renewable energy sources
Ullah et al. Deep Learning‐Assisted Short‐Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU
Yen et al. Solar power prediction via support vector machine and random forest
El Maghraoui et al. Smart energy management system: A comparative study of energy consumption prediction algorithms for a hotel building