Anuar et al., 2021 - Google Patents
Determination of typical electricity load profile by using double clustering of fuzzy C-means and hierarchical methodAnuar et al., 2021
- Document ID
- 3669109850993893991
- Author
- Anuar N
- Baharin N
- Nizam N
- Fadzilah A
- Nazri S
- Lip N
- Publication year
- Publication venue
- 2021 IEEE 12th Control and System Graduate Research Colloquium (ICSGRC)
External Links
Snippet
The Fuzzy C-means (FCM) and Hierarchical clustering method are widely used by many researchers in clustering data sets of electricity consumption to determine the typical consumers' electricity load profile. FCM method clustered the data sets into several clusters …
- 230000005611 electricity 0 title abstract description 45
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
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- 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
- 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
- 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
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Michalakopoulos et al. | A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs | |
| Quilumba et al. | Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities | |
| Ramos et al. | A data-mining-based methodology to support MV electricity customers’ characterization | |
| CN110380444B (en) | Capacity planning method for distributed wind power orderly access to power grid under multiple scenes based on variable structure Copula | |
| Dragomir et al. | Matlab application of Kohonen self-organizing map to classify consumers’ load profiles | |
| Bedingfield et al. | Multi-granular electricity consumer load profiling for smart homes using a scalable big data algorithm | |
| Haq et al. | Classification of electricity load profile data and the prediction of load demand variability | |
| Justo et al. | Behavioral similarity of residential customers using a neural network based on adaptive resonance theory | |
| Krstev et al. | An overview of forecasting methods for monthly electricity consumption | |
| CN117559443A (en) | Orderly power consumption control method for large industrial user clusters under peak load | |
| Kang et al. | A novel physical-feature-based approach for stochastic simulation of typical building electricity use profiles | |
| Ghosh et al. | Cross-correlation based classification of electrical appliances for non-intrusive load monitoring | |
| Anuar et al. | Determination of typical electricity load profile by using double clustering of fuzzy C-means and hierarchical method | |
| Zhang et al. | The power big data-based energy analysis for intelligent community in smart grid | |
| Arco et al. | Clustering methodology for smart metering data based on local and global features | |
| CN118277747A (en) | Electricity consumption behavior analysis method and system based on artificial intelligence | |
| CN117743885A (en) | Output power combination prediction method for large-scale medium-small hydropower station cluster | |
| Vankov et al. | Clustering time series over electrical networks | |
| Watts et al. | Selecting Representative Net Load Profiles of Solar Homes With K-Means Clustering | |
| CN115358885A (en) | Platform area load identification and load response evaluation method based on deep learning | |
| Ma et al. | Photovoltaic time series aggregation method based on K-means and MCMC algorithm | |
| Benítez Sánchez et al. | Classification of customers based on temporal load profile patterns | |
| Gao et al. | Mining of Behavioral Characteristics of Power Users Based on Multidimensional User Profiles and Big Data | |
| CN120582261B (en) | New energy access scheduling method, equipment and medium for electric power system | |
| Zhang et al. | An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition |