Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine
By Can Wan, Zhao Xu, Pierre Pinson, Zhao Yang Dong, Kit Po Wong. IEEE Transactions on Power Systems, 2014
685 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/78f3f9eebdde8854e5a0e91bc71994f294140b56
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yoshua Bengio
- Sergey Levine
- Demis Hassabis
- Alexandre Lacoste
- Li Deng
- Natasha Jaques
- Konrad Paul Kording
- Jianfeng Gao
In Electrical and Electronic Engineering
- Jakub Nowotarski
- Rafal Weron
- Jianhui Wang
- Shahaboddin Shamshirband
- Demis Hassabis
- Sudheer Ch
- Chong Wen Tong
- Ali Mostafaeipour
Citations in the corpus
- RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution (2016, methodology): “Wan et al. (2014), using ELM, proposed a model for short-term probabilistic wind power forecasting in a wind farm in Australia.”
- Probabilistic Forecast for Multiple Wind Farms Based on Regular Vine Copulas (2018, methodology): “Machine learning algorithms directly providing prediction intervals were described in [13], [14].”
- Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting (2019, background): “The energy produced by a wind generator is directly determined by the wind speed; therefore, accurate and efficient wind speed forecasting models would greatly benefit the wind energy prediction algorithms and consequently improves the reliability and security of the power grid [4],[5].”
- Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, background): “4, we present three proper scoring rules that have seen limited use in probabilistic energy forecasting [3, 5, 6, 19, 29, 32, 50, 58, 72, 100, 101] and definitely deserve to be recommended.”
- Short-term load forecasting based on multivariate time series prediction and weighted neural network with random weights and kernels (2018, background): “It has been bounded not only in the scientific research field but also in the engineering field [11,12].”
- Tackling Climate Change with Machine Learning (2019, methodology): “[43] Can Wan, Zhao Xu, Pierre Pinson, Zhao Yang Dong, and Kit Po Wong.”