RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment

By Zhang Yun, Z. Quan, Caixin Sun, Shaolan Lei, Yuming Liu, Song Yang. 2008

414 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

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

1. Muhammad Naveed Akhter
2. Shu Fan
3. Zhao Yang Dong
4. Rob J Hyndman
5. Hazlie Mokhlis
6. Noraisyah Mohamed Shah
7. David John Hill
8. Yan Xu

In Electrical and Electronic Engineering

1. Rob J Hyndman
2. Shu Fan
3. Muhammad Naveed Akhter
4. Zhao Yang Dong
5. Hazlie Mokhlis
6. Noraisyah Mohamed Shah
7. David John Hill
8. Yan Xu

In Management Science and Operations Research

1. Rob J Hyndman
2. Shu Fan
3. Muhammad Naveed Akhter
4. Hazlie Mokhlis
5. Noraisyah Mohamed Shah
6. David John Hill
7. Saad Mekhilef
8. Han Lin Shang

Citations in the corpus

• Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network (2019, result): “short-term load forecasting on the grid level [9]–[11], we should also benchmark the results of our proposed framework against those from the conventional backpropagation neural”
• Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, methodology): “ANFIS is used to forecast nonlinear values, in which previous sample information is used to forecast the sample in advance [80].”
• Short-Term Load Forecasting Based on a Semi-Parametric Additive Model (2012, background): “To incorporate the nonlinearity of electricity demand series, artificial neural networks (ANNs) have also received substantial attention in load forecasting with good performance reported [1], [8]–[14].”