Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines

By Fazil Kaytez, M. Taplamacıoğlu, Ertuğrul Çam, Fırat Hardalaç. 2015

453 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/11755447ab2a99d3509a0633e6e5b94120851a02

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. Ljupco Kocarev
2. Sonja Filiposka
3. Aleksandar Dedinec
4. Aleksandra Dedinec

In Electrical and Electronic Engineering

1. Ljupco Kocarev
2. Jaewook Lee
3. Sonja Filiposka
4. Aleksandar Dedinec
5. Aleksandra Dedinec
6. Huisu Jang

In Management Science and Operations Research

1. Ljupco Kocarev
2. Jaewook Lee
3. Sonja Filiposka
4. Aleksandar Dedinec
5. Aleksandra Dedinec
6. Huisu Jang

Citations in the corpus

• Deep belief network based electricity load forecasting: An analysis of Macedonian case (2016, background): “[13] where the set of parameters includes the gross electricity generation, installed capacity, total subscribership and population.”
• An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information (2018, background): “Reference [25] showed that the least squares support vector machines has better prediction performance for the time series of electrical energy consumption of Turkey compared to the traditional regression models and artificial neural networks.”