Smart Meter Data Analytics Electricity Consumer Behavior Modeling, Aggregation, and Forecasting /
| Hoofdauteurs: | , , |
|---|---|
| Coauteur: | |
| Samenvatting: | XXI, 293 p. 141 illus., 125 illus. in color. text |
| Taal: | Engels |
| Gepubliceerd in: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2020.
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| Editie: | 1st ed. 2020. |
| Onderwerpen: | |
| Online toegang: | https://doi.org/10.1007/978-981-15-2624-4 |
| Formaat: | Elektronisch Boek |
Inhoudsopgave:
- Overview for Smart Meter Data Analytics
- Smart Meter Data Compression Based on Load Feature Identification
- A Combined Data-Driven Approach for Electricity Theft Detection
- GAN-based Model for Residential Load Generation
- Ensemble Clustering for Individual Electricity Consumption Patterns Extraction
- Sparse and Redundant Representation-Based Partial Usage Pattern Extraction
- Data-Driven Personalized Price Design in Retail Market Using Smart Meter Data
- Deep Learning-Based Socio-demographic Information Identification
- Cross-domain Feature Selection and Coding for Household Energy Behavior
- Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications
- Enhancing Short-term Probabilistic Residential Load Forecasting with Quantile LSTM
- An Ensemble Forecasting Method for the Aggregated Load With Subprofiles
- Prospects of Future Research Issues on Smart Meter Data Analytics.