A Temporal Neural Network Model for Probabilistic Multi-Period Forecasting of Distributed Energy Resources
Probabilistic forecasts of electrical loads and photovoltaic generation provide a family of methods able to incorporate uncertainty estimations in predictions. This paper aims to extend the literature on these methods by proposing a novel deep-learning model based on a mixture of convolutional neura...
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/0373dab8d2c44356b1f51940e950d2ed |
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