Energy demand estimation of South Korea using artificial neural network

By Zong Woo Geem, W. E. Roper. 2009

206 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b67f47ac1423739705229709f1c4db5a513ea6bc

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. Ali Nazarpour
2. S. Sadegh
3. Sasan Barak
4. Reza Toorajipour
5. Yi Zeng
6. Lin Wang
7. Pejvak Oghazi
8. Tao Chen

In Electrical and Electronic Engineering

1. S. Sadegh
2. Sasan Barak
3. Yi Zeng
4. Lin Wang
5. Tao Chen

In Management Science and Operations Research

1. Ali Nazarpour
2. S. Sadegh
3. Sasan Barak
4. Reza Toorajipour
5. Yi Zeng
6. Lin Wang
7. Pejvak Oghazi
8. Tao Chen

Citations in the corpus

• Back propagation neural network with adaptive differential evolution algorithm for time series forecasting (2015, methodology): “Artificial neural networks (ANNs) have been extensively studied and used in time series forecasting (Adebiyi, Adewumi, & Ayo, 2014; Bennett, Stewart, & Beal, 2013; Geem & Roper, 2009; Zhang, Patuwo, & Hu, 2001; Zhang & Qi, 2005).”
• Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm (2016, methodology): “It is clear from the related literature [57-59] that usually 4 independent variables, including: population, gross domestic production, import and export, are used as inputs to the models in energy consumption forecasting.”
• Artificial intelligence in supply chain management: A systematic literature review (2021, background): “Geem and Roper (2009) propose an artificial neural network model to efficiently estimate the energy demand.”