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

Yan et al., 2022 - Google Patents

Wind speed prediction using a hybrid model of EEMD and LSTM considering seasonal features

Yan et al., 2022

View HTML
Document ID
16088829033402659193
Author
Yan Y
Wang X
Ren F
Shao Z
Tian C
Publication year
Publication venue
Energy Reports

External Links

Snippet

As a clean and renewable energy source, wind power is of great significance for addressing global energy shortages and environmental pollution. However, the uncertainty of wind speed hinders the direct use of wind power, resulting in a high proportion of abandoned …
Continue reading at www.sciencedirect.com (HTML) (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
    • G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • 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
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Yan et al. Wind speed prediction using a hybrid model of EEMD and LSTM considering seasonal features
Tian et al. A novel decomposition-ensemble prediction model for ultra-short-term wind speed
Liu et al. A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting
Zhao et al. Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features
Duan et al. Short-term wind speed forecasting using recurrent neural networks with error correction
Ma et al. A hybrid attention-based deep learning approach for wind power prediction
Yan et al. Multistep forecasting for diurnal wind speed based on hybrid deep learning model with improved singular spectrum decomposition
Tian et al. A combination forecasting model of wind speed based on decomposition
Du et al. Time series forecasting using sequence-to-sequence deep learning framework
Zhang et al. Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model
Wang et al. A novel framework for carbon price prediction using comprehensive feature screening, bidirectional gate recurrent unit and Gaussian process regression
Yang et al. Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach
Yu et al. Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification
Liu et al. A novel hybrid model based on GA-VMD, sample entropy reconstruction and BiLSTM for wind speed prediction
Tong et al. Hourly solar irradiance forecasting based on encoder–decoder model using series decomposition and dynamic error compensation
Hanifi et al. Offshore wind power forecasting based on WPD and optimised deep learning methods
Liu et al. A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM
Wang et al. A novel wind power prediction model improved with feature enhancement and autoregressive error compensation
Liu et al. Super multi-step wind speed forecasting system with training set extension and horizontal–vertical integration neural network
Faraz et al. Stock market prediction-by-prediction based on autoencoder long short-term memory networks
Fu et al. Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and LSTM
Feng et al. Multi-step-ahead solar output time series prediction with gate recurrent unit neural network using data decomposition and cooperation search algorithm
Yang et al. An ensemble prediction system based on artificial neural networks and deep learning methods for deterministic and probabilistic carbon price forecasting
Yu et al. Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China
Wang et al. Robust multi-step wind speed forecasting based on a graph-based data reconstruction deep learning method