Li et al., 2021 - Google Patents
GMM-HMM-based medium-and long-term multi-wind farm correlated power output time series generation methodLi et al., 2021
View PDF- Document ID
- 11095829458442216004
- Author
- Li Y
- Hu B
- Niu T
- Gao S
- Yan J
- Xie K
- Ren Z
- Publication year
- Publication venue
- IEEE Access
External Links
Snippet
Medium-and long-term wind power output time series are required in stochastic programming model for power system planning. Hidden Markov model (HMM) is a common method to generate wind power output time series, which can simultaneously consider the …
- 230000002596 correlated 0 title abstract description 4
Classifications
-
- 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
-
- 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
- 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
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
-
- 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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- 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
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- G06Q30/0202—Market predictions or demand forecasting
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Sun et al. | Using Bayesian deep learning to capture uncertainty for residential net load forecasting | |
Xie et al. | A nonparametric Bayesian framework for short-term wind power probabilistic forecast | |
Peng et al. | EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning | |
WO2022135265A1 (en) | Failure warning and analysis method for reservoir dispatching rules under effects of climate change | |
Sun et al. | Clustering-based residential baseline estimation: A probabilistic perspective | |
Cao et al. | Hybrid ensemble deep learning for deterministic and probabilistic low-voltage load forecasting | |
Li et al. | GMM-HMM-based medium-and long-term multi-wind farm correlated power output time series generation method | |
Li et al. | Energy data generation with wasserstein deep convolutional generative adversarial networks | |
Cheng et al. | Enhanced state estimation and bad data identification in active power distribution networks using photovoltaic power forecasting | |
CN109142171A (en) | The city PM10 concentration prediction method of fused neural network based on feature expansion | |
CN114297036B (en) | Data processing method, device, electronic equipment and readable storage medium | |
Sørensen et al. | Recent developments in multivariate wind and solar power forecasting | |
Fan et al. | Fluctuation pattern recognition based ultra-short-term wind power probabilistic forecasting method | |
CN113592144B (en) | Medium-long term runoff probability forecasting method and system | |
Li et al. | A novel combined prediction model for monthly mean precipitation with error correction strategy | |
CN110956309A (en) | Flow activity prediction method based on CRF and LSTM | |
Galbally et al. | A pattern recognition approach based on DTW for automatic transient identification in nuclear power plants | |
CN115034485A (en) | Wind power interval prediction method and device based on data space | |
CN113111592A (en) | Short-term wind power prediction method based on EMD-LSTM | |
Amarasinghe et al. | Kernel density estimation based time-dependent approach for analyzing the impact of increasing renewables on generation system adequacy | |
Yang et al. | Teacher–Student Uncertainty Autoencoder for the Process-Relevant and Quality-Relevant Fault Detection in the Industrial Process | |
Shi et al. | Deep-learning-based wind speed forecasting considering spatial–temporal correlations with adjacent wind turbines | |
Wibawa et al. | Bidirectional Long Short-Term Memory (Bi-LSTM) Hourly Energy Forecasting | |
Xu et al. | NWP feature selection and GCN-based ultra-short-term wind farm cluster power forecasting method | |
Mokilane et al. | Bayesian structural time-series approach to a long-term electricity demand forecasting |