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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 online resource (324 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
building electric energy consumption forecasting
bus load forecasting cold-start problem combined model component estimation method convolution neural network cost analysis cubic splines data augmentation data preprocessing technique day ahead DBN deep learning deep residual neural network demand response demand-side management distributed energy resources electric load forecasting electricity electricity consumption electricity demand feature extraction feature selection forecasting hierarchical short-term load forecasting hybrid energy system lasso load forecasting Load forecasting load metering long short-term memory modeling and forecasting multiobjective optimization algorithm multiple sources multivariate random forests Nordic electricity market pattern similarity performance criteria power systems preliminary load prosumers PSR random forest real-time electricity load regressive models residential load forecasting seasonal patterns short term load forecasting short-term load forecasting special days Tikhonov regularization time series transfer learning univariate and multivariate time series analysis VSTLF wavenet weather station selection |
| 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 online resource (128 p.) |
| Soggetto non controllato |
?1 lasso
?2 ridge accuracy adapative lasso adaptive lasso allele read counts AUROC BH-FDR data envelopment analysis elastic net ensembles entropy feature selection gene expression data gene-expression data geometric distribution geometric mean Gompertz distribution group efficiency comparison high-throughput Kullback-Leibler divergence Laplacian matrix lasso LASSO low-coverage MCP mixture model next-generation sequencing probability proportional to size (PPS) sampling randomization device resampling sandwich variance estimator SCAD sea surface temperature semiparametric regression sensitive attribute SIS Skew-Reflected-Gompertz distribution stochastic frontier model Yennum et al.'s model |
| 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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