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Short-Term Load Forecasting 2019
| Short-Term Load Forecasting 2019 |
| Autore | Gabaldón Antonio |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (324 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
short-term load forecasting
demand-side management pattern similarity hierarchical short-term load forecasting feature selection weather station selection load forecasting special days regressive models electric load forecasting data preprocessing technique multiobjective optimization algorithm combined model Nordic electricity market electricity demand component estimation method univariate and multivariate time series analysis modeling and forecasting deep learning wavenet long short-term memory demand response hybrid energy system data augmentation convolution neural network residential load forecasting forecasting time series cubic splines real-time electricity load seasonal patterns Load forecasting VSTLF bus load forecasting DBN PSR distributed energy resources prosumers building electric energy consumption forecasting cold-start problem transfer learning multivariate random forests random forest electricity consumption lasso Tikhonov regularization load metering preliminary load short term load forecasting performance criteria power systems cost analysis day ahead feature extraction deep residual neural network multiple sources electricity |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557494303321 |
Gabaldón Antonio
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Uncertainty Quantification Techniques in Statistics
| Uncertainty Quantification Techniques in Statistics |
| Autore | Kim Jong-Min |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (128 p.) |
| Soggetto non controllato |
Kullback–Leibler divergence
geometric distribution accuracy AUROC allele read counts mixture model low-coverage entropy gene-expression data SCAD data envelopment analysis LASSO high-throughput sandwich variance estimator adaptive lasso semiparametric regression ?1 lasso Laplacian matrix elastic net feature selection sea surface temperature gene expression data Skew-Reflected-Gompertz distribution lasso next-generation sequencing BH-FDR stochastic frontier model ?2 ridge geometric mean resampling Gompertz distribution adapative lasso group efficiency comparison sensitive attribute MCP probability proportional to size (PPS) sampling randomization device SIS Yennum et al.’s model ensembles |
| ISBN | 3-03928-547-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404091103321 |
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Kim Jong-Min
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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