Documentos de Srinivasan D. | REDI

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Mostrando 7 resultados de: 7

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Área de conocimiento: "Aprendizaje automático"

Subtipo de publicación

Conference Object(2)

Publisher

Applied Energy(1)

Asia-Pacific Power and Energy Engineering Conference, APPEEC(1)

IEEE Transactions on Neural Networks and Learning Systems(1)

IEEE Transactions on Sustainable Energy(1)

International Conference on Innovative Smart Grid Technologies, ISGT Asia 2018(1)

Área temáticas

Ciencias de la computación(5)

Física aplicada(2)

Métodos informáticos especiales(2)

Otras ramas de la ingeniería(1)

Área de conocimiento

Energía renovable(7)

Energía solar(1)

Fotovoltaica(1)

Meteorología(1)

Sensores remotos(1)

Año de Publicación

Origen

Palabras Claves

Ensemble forecasting(2)

Machine learning(2)

probabilistic forecasting(2)

Automatic machine learning, Solar forecasting, R caret package(1)

Convolutional neural network(1)

Automatic hourly solar forecasting using machine learning models

Abstract: Owing to its recent advance, machine learning has spawned a large collection of solar forecasting wo

Palabras claves:

Automatic machine learning, Solar forecasting, R caret package

Dazhi Yang, Srinivasan D., Yagli G.M.

An ensemble machine learning based approach for constructing probabilistic PV generation forecasting

Conference Object

Abstract: Photovoltaic (PV) generation forecasting plays an important role in accommodating more distributed P

Palabras claves:

pbkp_rediction interval, Photovoltaic, probabilistic forecasting, Quantile regression, Stochastic gradient boosting machine, UNCERTAINTY

Carlos David Rodríguez-Gallegos, Gandhi O., Quan H., Sharma A., Srinivasan D., Zhang W.

A Survey of Computational Intelligence Techniques for Wind Power Uncertainty Quantification in Smart Grids

Abstract: The high penetration level of renewable energy is thought to be one of the basic characteristics of

Palabras claves:

computational intelligence, Decision-making, Neural Network (NN), pbkp_rediction interval (PI), uncertainty quantification, Wind power

Dazhi Yang, Khosravi A., Quan H., Srinivasan D.

Deep-learning-based probabilistic estimation of solar PV soiling loss

Abstract: Although the integration of solar photovoltaic (PV) systems is gaining widespread acceptance, the in

Palabras claves:

Convolutional neural network, deep learning, Photovoltaic (PV) system, probabilistic estimation, solar PV panel soiling

Carlos David Rodríguez-Gallegos, Gandhi O., Liu S., Quan H., Srinivasan D., Zhang W.

Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance?

Abstract: • Independently generated ground-based and satellite-based forecasts are compared. • The joint distr

Palabras claves:

Ensemble forecasting, Machine learning, Satellite-derived irradiance, solar forecasting

Dazhi Yang, Gandhi O., Srinivasan D., Yagli G.M.

Ensemble solar forecasting using data-driven models with probabilistic post-processing through GAMLSS

Abstract: Forecast performance of data-driven models depends on the local weather and climate regime, which ma

Palabras claves:

Ensemble forecasting, Forecast combination, Machine learning, Post-processing, probabilistic forecasting, TIME SERIES

Dazhi Yang, Srinivasan D., Yagli G.M.

Using Combinational Methods for Forecast Improvement in PV Power Plants

Conference Object

Abstract: Power generation based on photovoltaic systems are one of the crucial energy resources of the future

Palabras claves:

Dazhi Yang, Monika , Srinivasan D., Yagli G.M.