Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher

By Kostas S. Metaxiotis, A. G. Kagiannas, D. Askounis, J. Psarras. 2003

295 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/418fc00111154346e04b368752e583d0d3a72a43

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Ping-Huan Kuo
2. Chiou-Jye Huang

In Management Science and Operations Research

1. Ping-Huan Kuo
2. Chiou-Jye Huang

Citations in the corpus

• A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting (2018, result): “When compared with the traditional statistical forecasting methods, the ability to analyze large amounts of data in a very short time frame using AI technology has displayed obvious advantages [10].”