Using adaptive network based fuzzy inference system to forecast regional electricity loads
By Li-Chih Ying, Mei Pan. 2008
279 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Environmental Engineering
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
- Ye Ren
- Ponnuthurai Nagaratnam Suganthan
- Gehan A. J. Amaratunga
- Xueheng Qiu
- Biswajeet Pradhan
- Yao Dong
- Jianzhou Wang
- Ge Zhao
In Electrical and Electronic Engineering
- Ponnuthurai Nagaratnam Suganthan
- Yao Dong
- Gehan A. J. Amaratunga
- Jianzhou Wang
- Ge Zhao
- Yuanyuan Wang
- Milos Manic
- Kasun Amarasinghe
In Environmental Engineering
- Biswajeet Pradhan
- D. Tien Bui
- Yao Dong
- Inge Revhaug
- Jianzhou Wang
- Ge Zhao
- Yuanyuan Wang
- Hamid Reza Pourghasemi
Citations in the corpus
- Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China (2012, methodology): “For example, according to the research of Bunn and Farmer (1985), the operating cost will increase by 10 million pounds every year for every 1% increase in the forecasting error (Ying and Pan, 2008).”
- Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting (2017, background): “Integrated Moving computational intelligence evaluation [10] widely used for solution for short-term includes pattern forecasting term load applied in the al.”
- Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS (2012, methodology): “Hybrid learning algorithm The hybrid learning algorithm of the ANFIS is a combination of the gradient method and the least squares method to adjust the parameters in an adaptive network (Ying and Pan, 2008).”