AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network

By K. Bhaskar, S. Singh. IEEE Transactions on Sustainable Energy, 2012

372 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1114ad3e390d83dc6b134038ae8f6185e5563282

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jianhui Wang
2. Mahdi Khodayar
3. Zhao Yang Dong
4. Okyay Kaynak
5. Pierre Pinson
6. Robert P. Broadwater
7. Mohammad E. Khodayar
8. Jaesung Jung

In Electrical and Electronic Engineering

1. Jianhui Wang
2. Mahdi Khodayar
3. Can Wan
4. Zhao Yang Dong
5. Kit Po Wong
6. Zhao Xu
7. Yu Zheng
8. Ke Meng

Citations in the corpus

• Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity (2016, neutral): “Bhaskar and Singh (2012) take a statistical approach which does not use numerical weather predictions.”
• Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting (2017, background): “In ANN models, most neural architectures have only one single hidden layer [11]–[15].”
• Current status and future advances for wind speed and power forecasting (2014, methodology): “Similarly, in [55], Adaptive Wavelet Neural Network (AWNN) is carried out to forecast the wind speed in stage-I.”
• Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations (2014, background): “It is a basic task to support the decision of electricity procurement [24], [25] and can facilitate renewable penetrations [26], [27].”
• Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine (2014, background): “Due to the excellent approximation and generalization capabilities, neural networks (NNs) are widely used for wind power forecasts [23]–[25], irrespective of some drawbacks like local minima, overtraining, and high computational costs.”
• Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting (2019, methodology): “Most recently proposed ANN-based approaches design and learn shallow neural architectures with few levels of abstraction, such as Feed-forward ANN [17][18], recurrent ANN [19], RBF network [20]-[21], ridgelet ANN [22] and adaptive wavelet ANN [23].”