Zhang et al., 2020 - Google Patents
Load Prediction Based on Hybrid Model of VMD‐mRMR‐BPNN‐LSSVMZhang et al., 2020
View PDF- Document ID
- 16350307913804370593
- Author
- Zhang G
- Liu H
- Li P
- Li M
- He Q
- Chao H
- Zhang J
- Hou J
- Publication year
- Publication venue
- Complexity
External Links
Snippet
Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi‐frequency. In order to improve the prediction accuracy, this …
- 238000000354 decomposition reaction 0 abstract description 30
Classifications
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- 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
-
- 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
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Investment, e.g. financial instruments, portfolio management or fund management
-
- 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
-
- 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
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Alhussein et al. | Hybrid CNN-LSTM model for short-term individual household load forecasting | |
Nazir et al. | Forecasting energy consumption demand of customers in smart grid using Temporal Fusion Transformer (TFT) | |
Tan et al. | A multi-task learning method for multi-energy load forecasting based on synthesis correlation analysis and load participation factor | |
Tahmasebifar et al. | Point and interval forecasting of real‐time and day‐ahead electricity prices by a novel hybrid approach | |
Zhang et al. | Load Prediction Based on Hybrid Model of VMD‐mRMR‐BPNN‐LSSVM | |
Dou et al. | Hybrid model for renewable energy and loads prediction based on data mining and variational mode decomposition | |
Cai et al. | Short‐term load forecasting method based on deep neural network with sample weights | |
Kong et al. | Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants | |
Liu et al. | Adaptive wavelet transform model for time series data prediction | |
CN113591368A (en) | Comprehensive energy system multi-energy load prediction method and system | |
Souhe et al. | A hybrid model for forecasting the consumption of electrical energy in a smart grid | |
Yu et al. | A novel short-term electrical load forecasting framework with intelligent feature engineering | |
CN114943565A (en) | Electric power spot price prediction method and device based on intelligent algorithm | |
Liu et al. | Short‐term load forecasting based on LSTNet in power system | |
Gao et al. | A two-layer SSA-XGBoost-MLR continuous multi-day peak load forecasting method based on hybrid aggregated two-phase decomposition | |
Zhang et al. | Prediction method of line loss rate in low‐voltage distribution network based on multi‐dimensional information matrix and dimensional attention mechanism‐long‐and short‐term time‐series network | |
Liang et al. | A wind speed combination forecasting method based on multifaceted feature fusion and transfer learning for centralized control center | |
Li et al. | An intelligent method for wind power forecasting based on integrated power slope events prediction and wind speed forecasting | |
Flesca et al. | On forecasting non-renewable energy production with uncertainty quantification: A case study of the Italian energy market | |
You et al. | A novel approach for CPU load prediction of cloud server combining denoising and error correction | |
Oprea et al. | Big data processing for commercial buildings and assessing flexibility in the context of citizen energy communities | |
Zhang et al. | Load forecasting method based on improved deep learning in cloud computing environment | |
Zhu | Research on adaptive combined wind speed prediction for each season based on improved gray relational analysis | |
Wu et al. | Forecast of short‐term electricity price based on data analysis | |
Yang et al. | An improved spatial upscaling method for producing day‐ahead power forecasts for wind farm clusters |