Bigdeli et al., 2013 - Google Patents
A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, CanadaBigdeli et al., 2013
- Document ID
- 16294325560418808171
- Author
- Bigdeli N
- Afshar K
- Gazafroudi A
- Ramandi M
- Publication year
- Publication venue
- Renewable and sustainable energy reviews
External Links
Snippet
In the recent years, by rapid growth of wind power generation in addition to its high penetration in power systems, the wind power prediction has been known as an important research issue. Wind power has a complicated dynamic for modeling and prediction. In this …
- 230000000052 comparative effect 0 title abstract description 5
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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Bigdeli et al. | A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada | |
Aly | An intelligent hybrid model of neuro Wavelet, time series and Recurrent Kalman Filter for wind speed forecasting | |
Li et al. | A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting | |
Ren et al. | A fully data-driven method based on generative adversarial networks for power system dynamic security assessment with missing data | |
Zhou et al. | Multi-energy net load forecasting for integrated local energy systems with heterogeneous prosumers | |
Barak et al. | Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm | |
AlShafeey et al. | Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods | |
Zhang et al. | Improving probabilistic load forecasting using quantile regression NN with skip connections | |
Catalao et al. | An artificial neural network approach for short-term wind power forecasting in Portugal | |
Li et al. | Energy data generation with wasserstein deep convolutional generative adversarial networks | |
Alekseev et al. | A multivariate neural forecasting modeling for air transport–preprocessed by decomposition: a Brazilian application | |
Samet et al. | Quantizing the deterministic nonlinearity in wind speed time series | |
CN104951836A (en) | Posting predication system based on nerual network technique | |
Bu et al. | Hybrid short-term load forecasting using CGAN with CNN and semi-supervised regression | |
Sani Salisu et al. | Solar radiation forecasting in Nigeria based on hybrid PSO-ANFIS and WT-ANFIS approach | |
Samet et al. | Evaluation of neural network-based methodologies for wind speed forecasting | |
Niu et al. | Short‐term wind speed hybrid forecasting model based on bias correcting study and its application | |
Kamel et al. | On the Performance Robustness of Artificial Neural Network Approaches and Gumbel Extreme Value Distribution for Prediction of Wind Speed | |
Zhou et al. | A two-stage method for ultra-short-term pv power forecasting based on data-driven | |
Oladipo et al. | Enhanced adaptive neuro-fuzzy inference system using genetic algorithm: A case study in predicting electricity consumption | |
Yousefi et al. | Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN | |
Rahaman et al. | Bayesian optimization based ANN model for short term wind speed forecasting in newfoundland, Canada | |
Gu et al. | Fuzzy time series forecasting based on information granule and neural network | |
Raphel | Artificial intelligence‐based wind forecasting using variational mode decomposition. | |
Dan et al. | Application of machine learning in forecasting energy usage of building design |