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

Wan et al., 2019 - Google Patents

Turbine location wind speed forecast using convolutional neural network

Wan et al., 2019

Document ID
7574497451478405226
Author
Wan T
Li H
Wang C
Kou P
Publication year
Publication venue
2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP)

External Links

Snippet

Traditional wind speed forecast usually regards wind farm as a point to make forecast, but in a wind farm, wind speed of wind turbines in different geographical locations is not the same. For many wind turbines with wide geographical distribution in a wind farm, this paper gives a …
Continue reading at ieeexplore.ieee.org (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
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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
    • 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
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • 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

Similar Documents

Publication Publication Date Title
Shamshirband et al. A survey of deep learning techniques: application in wind and solar energy resources
Hong et al. Day-ahead solar irradiation forecasting utilizing gramian angular field and convolutional long short-term memory
Li et al. Photovoltaic power forecasting with a hybrid deep learning approach
Zang et al. Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network
Wang et al. Short-term wind speed forecasting based on information of neighboring wind farms
Pan et al. Short-term wind speed forecasting based on spatial-temporal graph transformer networks
Zhu et al. Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN
Ghaderi et al. Deep forecast: Deep learning-based spatio-temporal forecasting
Zhou et al. Transfer learning for photovoltaic power forecasting with long short-term memory neural network
Lawal et al. Wind speed prediction using hybrid 1D CNN and BLSTM network
CN112561058B (en) Short-term photovoltaic power prediction method based on Stacking-integrated learning
Yu et al. Prediction of solar irradiance one hour ahead based on quantum long short-term memory network
Eseye et al. A double-stage hierarchical hybrid PSO-ANN model for short-term wind power prediction
He et al. A power forecasting approach for PV plant based on irradiance index and LSTM
Tuyen et al. A combination of novel hybrid deep learning model and quantile regression for short‐term deterministic and probabilistic PV maximum power forecasting
Lin et al. GAOformer: An adaptive spatiotemporal feature fusion transformer utilizing GAT and optimizable graph matrixes for offshore wind speed prediction
Dhanalakshmi et al. [Retracted] Application of Machine Learning in Multi‐Directional Model to Follow Solar Energy Using Photo Sensor Matrix
Huang et al. Short-term pv power forecasting based on ceemdan and ensemble deeptcn
Wan et al. Turbine location wind speed forecast using convolutional neural network
Afrasiabi et al. Nonparametric Maximum Likelihood Probabilistic Photovoltaic Power Generation Forecasting based on Spatial-Temporal Deep Learning
Wang et al. Ultra-short-term regional PV power forecasting based on fluctuation pattern recognition with satellite images
EP4383498A1 (en) Method of forecasting power production/consumption in a distributed network
CN117035446A (en) Ultra-short-term photovoltaic output prediction method considering space-time correlation among arrays
Zhang et al. Prediction of direct normal irradiance based on ensemble deep learning models
Nguyen-Duc et al. Multi 2D-CNN-based model for short-term PV power forecast embedded with Laplacian Attention