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

Liang et al., 2022 - Google Patents

A wind speed combination forecasting method based on multifaceted feature fusion and transfer learning for centralized control center

Liang et al., 2022

Document ID
11062296375922433693
Author
Liang T
Chen C
Mei C
Jing Y
Sun H
Publication year
Publication venue
Electric Power Systems Research

External Links

Snippet

With the establishment of remote centralized control centers for wind farms, the combined model of multiple deep neural networks used in most wind speed prediction methods can no longer meet the requirements of centralized control centers for efficient and low-cost wind …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • 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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • 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
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • 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
    • 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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • 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

Similar Documents

Publication Publication Date Title
Tang et al. Short-term load forecasting using channel and temporal attention based temporal convolutional network
Fekri et al. Distributed load forecasting using smart meter data: Federated learning with Recurrent Neural Networks
Atef et al. Assessment of stacked unidirectional and bidirectional long short-term memory networks for electricity load forecasting
Shen et al. Wind speed prediction of unmanned sailboat based on CNN and LSTM hybrid neural network
Hong et al. SVR with hybrid chaotic genetic algorithms for tourism demand forecasting
Nazir et al. Forecasting energy consumption demand of customers in smart grid using Temporal Fusion Transformer (TFT)
Lu et al. A short-term load forecasting model based on mixup and transfer learning
Niu et al. Uncertainty modeling for chaotic time series based on optimal multi-input multi-output architecture: Application to offshore wind speed
Zeng et al. Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM
Kong et al. Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants
Na et al. Hierarchical delay-memory echo state network: A model designed for multi-step chaotic time series prediction
Liao et al. Short-term power prediction for renewable energy using hybrid graph convolutional network and long short-term memory approach
Liang et al. A wind speed combination forecasting method based on multifaceted feature fusion and transfer learning for centralized control center
Safari et al. Multi-term electrical load forecasting of smart cities using a new hybrid highly accurate neural network-based predictive model
Wang et al. Deep autoencoder with localized stochastic sensitivity for short-term load forecasting
Yu et al. A novel short-term electrical load forecasting framework with intelligent feature engineering
Rabie et al. A fog based load forecasting strategy based on multi-ensemble classification for smart grids
Zhang et al. Load Prediction Based on Hybrid Model of VMD‐mRMR‐BPNN‐LSSVM
Chen et al. MultiCycleNet: multiple cycles self-boosted neural network for short-term electric household load forecasting
Xing et al. Real-time optimal scheduling for active distribution networks: A graph reinforcement learning method
Shen et al. An active learning-based incremental deep-broad learning algorithm for unbalanced time series prediction
Al-Ja’afreh et al. An enhanced CNN-LSTM based multi-stage framework for PV and load short-term forecasting: DSO scenarios
CN115759458A (en) Load prediction method based on comprehensive energy data processing and multi-task deep learning
Cao et al. A hybrid electricity load prediction system based on weighted fuzzy time series and multi-objective differential evolution
Wasesa et al. Predicting electricity consumption in microgrid-based educational building using google trends, google mobility, and covid-19 data in the context of covid-19 pandemic