[HTML][HTML] Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain
Wind power forecasting has supported operational decision-making for power system and
electricity markets for 30 years. Efforts of improving the accuracy and/or certainty of …
electricity markets for 30 years. Efforts of improving the accuracy and/or certainty of …
A comprehensive review on deep learning approaches in wind forecasting applications
The effective use of wind energy is an essential part of the sustainable development of
human society, in particular, at the recent unprecedented pressure in sha** a low carbon …
human society, in particular, at the recent unprecedented pressure in sha** a low carbon …
Deep learning based ensemble approach for probabilistic wind power forecasting
Due to the economic and environmental benefits, wind power is becoming one of the more
promising supplements for electric power generation. However, the uncertainty exhibited in …
promising supplements for electric power generation. However, the uncertainty exhibited in …
Deep concatenated residual network with bidirectional LSTM for one-hour-ahead wind power forecasting
This paper presents a deep residual network for improving time-series forecasting models,
indispensable to reliable and economical power grid operations, especially with high shares …
indispensable to reliable and economical power grid operations, especially with high shares …
Deep Learning for solar power forecasting—An approach using AutoEncoder and LSTM Neural Networks
Power forecasting of renewable energy power plants is a very active research field, as
reliable information about the future power generation allow for a safe operation of the …
reliable information about the future power generation allow for a safe operation of the …
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 …
Day-ahead self-scheduling of a virtual power plant in energy and reserve electricity markets under uncertainty
This paper proposes a novel model for the day-ahead self-scheduling problem of a virtual
power plant trading in both energy and reserve electricity markets. The virtual power plant …
power plant trading in both energy and reserve electricity markets. The virtual power plant …
A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting
W Zhang, Z Qu, K Zhang, W Mao, Y Ma… - Energy conversion and …, 2017 - Elsevier
Wind energy, which is stochastic and intermittent by nature, has a significant influence on
power system operation, power grid security and market economics. Precise and reliable …
power system operation, power grid security and market economics. Precise and reliable …
Transfer learning for short-term wind speed prediction with deep neural networks
Q Hu, R Zhang, Y Zhou - Renewable Energy, 2016 - Elsevier
As a type of clean and renewable energy source, wind power is widely used. However,
owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model …
owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model …
Probabilistic forecasting of wind power generation using extreme learning machine
Accurate and reliable forecast of wind power is essential to power system operation and
control. However, due to the nonstationarity of wind power series, traditional point …
control. However, due to the nonstationarity of wind power series, traditional point …