[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Wang et al., 2023 - Google Patents

A novel wind power prediction model improved with feature enhancement and autoregressive error compensation

Wang et al., 2023

Document ID
14375942577878669025
Author
Wang J
Zhu H
Cheng F
Zhou C
Zhang Y
Xu H
Liu M
Publication year
Publication venue
Journal of Cleaner Production

External Links

Snippet

Wind energy is a widely utilized form of clean energy with significant implications for maximizing its utilization and ensuring the stability of power systems. However, existing methods for short-term wind power prediction face challenges in effectively capturing hidden …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • 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
    • G06N3/08—Learning methods
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/70—Systems integrating technologies related to power network operation and communication or information technologies mediating in the improvement of the carbon footprint of electrical power generation, transmission or distribution, i.e. smart grids as enabling technology in the energy generation sector not used, see subgroups
    • Y02E60/76—Computer aided design [CAD]; Simulation; Modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • 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
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/54—Management of operational aspects, e.g. planning, load or production forecast, maintenance, construction, extension
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00—Communication or information technology specific aspects supporting electrical power generation, transmission, distribution or end-user application management
    • Y04S40/20—Information technology specific aspects
    • Y04S40/22—Computer aided design [CAD]; Simulation; Modelling

Similar Documents

Publication Publication Date Title
Wang et al. A novel wind power prediction model improved with feature enhancement and autoregressive error compensation
Zheng et al. A hybrid framework for forecasting power generation of multiple renewable energy sources
Gong et al. Short-term wind power forecasting model based on temporal convolutional network and Informer
CN112100911B (en) A Solar Radiation Prediction Method Based on Deep BILSTM
Zhang et al. Interval prediction of ultra-short-term photovoltaic power based on a hybrid model
Shi et al. Parallel deep prediction with covariance intersection fusion on non-stationary time series
Wang et al. Hybrid deep learning model for short-term wind speed forecasting based on time series decomposition and gated recurrent unit
Cui et al. The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm and attention mechanism
Mirza et al. A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model
Gong et al. Improving short-term offshore wind speed forecast accuracy using a VMD-PE-FCGRU hybrid model
Li et al. Research on a novel photovoltaic power forecasting model based on parallel long and short-term time series network
Lu et al. Hybrid machine learning models for predicting short-term wave energy flux
Li et al. Hybrid forecasting system considering the influence of seasonal factors under energy sustainable development goals
Liao et al. Wind power prediction based on periodic characteristic decomposition and multi-layer attention network
Wang et al. Considering the dual endogenous-exogenous uncertainty integrated energy multiple load short-term forecast
CN119476545A (en) A multi-element load forecasting method and system for electric-thermal-hydrogen multi-energy coupling system
Li et al. A novel transfer learning strategy for wind power prediction based on TimesNet-GRU architecture
Ding et al. Photovoltaic array power prediction model based on EEMD and PSO-KELM
Li et al. Regional ultra-short-term wind power combination prediction method based on fluctuant/smooth components division
Liu et al. Short-term wind power load forecasting based on ISSA-CNN-BiLSTM
Liu Research on machine learning-based solar energy production prediction
Zhang et al. Optimization of wind power generation prediction model based on machine learning
Jing et al. Research on PV power prediction based on VMD-BWO-KELM combination model
Yang et al. Ultra-short-term wind speed hybrid forecasting model based on maximal information coefficient-optimized TVF-EMD and resTKAN
Yang et al. A deep learning model based on multi-attention mechanism and gated recurrent unit network for photovoltaic power forecasting