A pareto optimization approach of a Gaussian process ensemble for short-term load forecasting

2011 ◽  
Author(s):  
Miltiadis Alamaniotis ◽  
Andreas Ikonomopoulos ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Gaussian Process ◽  
Load Forecasting ◽  
Pareto Optimization ◽  
Optimization Approach ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals Bayesian Spatiotemporal Gaussian Process for Short-term Load Forecasting Using Combined Transportation and Electricity Data

2020 ◽  
Vol 4 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Mostafa Gilanifar ◽  
Hui Wang ◽  
Eren Erman Ozguven ◽  
Yuxun Zhou ◽  
Reza Arghandeh
Keyword(s):  
Gaussian Process ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals Short-term load forecasting using Theta method

2019 ◽  
Vol 84 ◽  
pp. 01004 ◽  
Author(s):  
Grzegorz Dudek
Keyword(s):  
Empirical Studies ◽  
Load Forecasting ◽  
Seasonal Cycles ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Short Term Load Forecasting ◽  
Data Definition ◽  
System Data ◽  
Theta Method

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.

Sign in / Sign up

Export Citation Format

Share Document