Zhao et al., 2019 - Google Patents
Spatio‐temporal Markov chain model for very‐short‐term wind power forecastingZhao et al., 2019
View PDF- Document ID
- 16419095719583342212
- Author
- Zhao Y
- Ye L
- Wang Z
- Wu L
- Zhai B
- Lan H
- Yang S
- Publication year
- Publication venue
- The Journal of Engineering
External Links
Snippet
Wind power forecasting (WPF) is crucial in helping schedule and trade wind power generation at various spatial and temporal scales. With increasing number of wind farms over a region, research focus of WPF methods has been recently moved onto exploring …
- 230000002123 temporal effect 0 abstract description 11
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
- 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
- G06N3/12—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
-
- 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
-
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Karakuş et al. | One‐day ahead wind speed/power prediction based on polynomial autoregressive model | |
| Zhao et al. | Towards traffic matrix prediction with LSTM recurrent neural networks | |
| Eom et al. | Feature-selective ensemble learning-based long-term regional PV generation forecasting | |
| Yi et al. | A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting | |
| Rai et al. | A CNN‐BiLSTM based deep learning model for mid‐term solar radiation prediction | |
| Huang et al. | Short‐term wind power forecasting and uncertainty analysis using a hybrid intelligent method | |
| Sharma et al. | A novel two-stage framework for mid-term electric load forecasting | |
| Liu et al. | Takagi–Sugeno fuzzy model‐based approach considering multiple weather factors for the photovoltaic power short‐term forecasting | |
| Yu et al. | Improved convolutional neural network‐based quantile regression for regional photovoltaic generation probabilistic forecast | |
| Huang et al. | One‐day‐ahead hourly forecasting for photovoltaic power generation using an intelligent method with weather‐based forecasting models | |
| Jawad et al. | Genetic algorithm‐based non‐linear auto‐regressive with exogenous inputs neural network short‐term and medium‐term uncertainty modelling and prediction for electrical load and wind speed | |
| Zhao et al. | Spatio‐temporal Markov chain model for very‐short‐term wind power forecasting | |
| Afrasiabi et al. | Probabilistic deep neural network price forecasting based on residential load and wind speed predictions | |
| Wang et al. | Improved BP neural network algorithm to wind power forecast | |
| Yang et al. | A New Strategy for Short‐Term Load Forecasting | |
| Afrasiabi et al. | Deep learning architecture for direct probability density prediction of small‐scale solar generation | |
| Jogunuri et al. | Random forest machine learning algorithm based seasonal multi‐step ahead short‐term solar photovoltaic power output forecasting | |
| Souhe et al. | A hybrid model for forecasting the consumption of electrical energy in a smart grid | |
| Chen et al. | Combined probabilistic forecasting method for photovoltaic power using an improved Markov chain | |
| Wang et al. | Improving economic values of day‐ahead load forecasts to real‐time power system operations | |
| Zjavka | PV power intra‐day predictions using PDE models of polynomial networks based on operational calculus | |
| Chen et al. | Distribution feeder‐level day‐ahead peak load forecasting methods and comparative study | |
| Niu et al. | Short‐term wind speed hybrid forecasting model based on bias correcting study and its application | |
| He et al. | A per‐unit curve rotated decoupling method for CNN‐TCN based day‐ahead load forecasting | |
| Shan et al. | A deep‐learning based solar irradiance forecast using missing data |