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

Zhang et al., 2013 - Google Patents

Assessing long-term wind conditions by combining different measure-correlate-predict algorithms

Zhang et al., 2013

View PDF
Document ID
5900239834082493644
Author
Zhang J
Chowdhury S
Messac A
Hodge B
Publication year
Publication venue
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

External Links

Snippet

This paper significantly advanced the hybrid measure-correlate-predict (MCP) methodology, enabling it to account for the variations of both wind speed and direction. The advanced hybrid MCP method used the recorded data of multiple reference stations to estimate the …
Continue reading at www.osti.gov (PDF) (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
    • 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
    • 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
    • G06Q10/10Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Xie et al. A nonparametric Bayesian framework for short-term wind power probabilistic forecast
Liu et al. Wind power plant prediction by using neural networks
Davò et al. Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting
Sunderland et al. Small wind turbines in turbulent (urban) environments: A consideration of normal and Weibull distributions for power prediction
Li et al. Applications of Bayesian methods in wind energy conversion systems
Dong et al. Regional wind power probabilistic forecasting based on an improved kernel density estimation, regular vine copulas, and ensemble learning
Zhang et al. Assessing long-term wind conditions by combining different measure-correlate-predict algorithms
Masseran Markov chain model for the stochastic behaviors of wind-direction data
Wang et al. Wind speed frequency distribution modeling and wind energy resource assessment based on polynomial regression model
You et al. Direction-dependent power curve modeling for multiple interacting wind turbines
Zhang et al. A hybrid measure-correlate-predict method for long-term wind condition assessment
You et al. When wind travels through turbines: A new statistical approach for characterizing heterogeneous wake effects in multi-turbine wind farms
Richmond et al. Evaluation of an offshore wind farm computational fluid dynamics model against operational site data
Al-Duais et al. A unique Markov chain Monte Carlo method for forecasting wind power utilizing time series model
Wang et al. Toward a flexible scenario generation tool for stochastic renewable energy analysis
Jayashankara et al. A novel approach for short-term energy forecasting in smart buildings
Suwarno et al. Wind speed modeling based on measurement data to predict future wind speed with modified Rayleigh model
Zou et al. Evaluation of wind turbine power outputs with and without uncertainties in input wind speed and wind direction data
Ahmad et al. Efficient energy planning with decomposition-based evolutionary neural networks
Hasanien et al. Probabilistic optimal power flow in power systems with renewable energy integration using enhanced walrus optimization algorithm
Guo et al. Wind power assessment based on a WRF wind simulation with developed power curve modeling methods
Pan et al. A novel probabilistic modeling framework for wind speed with highlight of extremes under data discrepancy and uncertainty
Cabezon et al. Comparison of methods for power curve modelling
Li et al. An intelligent method for wind power forecasting based on integrated power slope events prediction and wind speed forecasting
Richter et al. Uncertainty quantification of offshore wind farms using Monte Carlo and sparse grid