Neural network-based uncertainty quantification: A survey of methodologies and applications
Uncertainty quantification plays a critical role in the process of decision making and
optimization in many fields of science and engineering. The field has gained an …
optimization in many fields of science and engineering. The field has gained an …
Review on probabilistic forecasting of wind power generation
Y Zhang, J Wang, X Wang - Renewable and Sustainable Energy Reviews, 2014 - Elsevier
The randomness and intermittence of wind resources is the biggest challenge in the
integration of wind power into the power system. Accurate forecasting of wind power …
integration of wind power into the power system. Accurate forecasting of wind power …
A hybrid deep learning-based neural network for 24-h ahead wind power forecasting
YY Hong, CLPP Rioflorido - Applied Energy, 2019 - Elsevier
Wind power generation is always associated with uncertainties as a result of fluctuations of
wind speed. Accurate predictions of wind power generation are important for the efficient …
wind speed. Accurate predictions of wind power generation are important for the efficient …
Deep belief network based deterministic and probabilistic wind speed forecasting approach
With the rapid growth of wind power penetration into modern power grids, wind speed
forecasting (WSF) plays an increasingly significant role in the planning and operation of …
forecasting (WSF) plays an increasingly significant role in the planning and operation of …
Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network
The penetration of photovoltaic (PV) energy into modern electric power and energy systems
has been gradually increased in recent years due to its benefits of being abundant …
has been gradually increased in recent years due to its benefits of being abundant …
Review of wind power scenario generation methods for optimal operation of renewable energy systems
J Li, J Zhou, B Chen - Applied Energy, 2020 - Elsevier
Scenario generation is an effective method for addressing uncertainties in stochastic
programming for energy systems with integrated wind power. To comprehensively …
programming for energy systems with integrated wind power. To comprehensively …
A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting
Large amounts of wind power generation have an impact not only on energy markets but
also on wholesale and retail market designs. Simultaneously, technological issues arise as …
also on wholesale and retail market designs. Simultaneously, technological issues arise as …
Direct quantile regression for nonparametric probabilistic forecasting of wind power generation
The fluctuation and uncertainty of wind power generation bring severe challenges to secure
and economic operation of power systems. Because wind power forecasting error is …
and economic operation of power systems. Because wind power forecasting error is …
Operation optimization of power to hydrogen and heat (P2HH) in ADN coordinated with the district heating network
Increasing percentages of distributed generators in active distribution networks (ADNs) have
increased the concern on excess generations in the medium and low voltage levels. High …
increased the concern on excess generations in the medium and low voltage levels. High …
Very short-term nonparametric probabilistic forecasting of renewable energy generation—With application to solar energy
Due to the inherent uncertainty involved in renewable energy forecasting, uncertainty
quantification is a key input to maintain acceptable levels of reliability and profitability in …
quantification is a key input to maintain acceptable levels of reliability and profitability in …