Non linear models and the load of an electricity distributor

• Published in: International journal of energy sector management Vol. 9; no. 1; pp. 38 - 56
• Main Authors: Simões, Mario Domingues, Klotzle, Marcelo Cabus, Pinto, Antonio Carlos Figueiredo, Gomes, Leonardo Lima
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Group Publishing Limited 07-04-2015
• Subjects: Ability tests; Computer applications; Economic models; Electricity; Energy; Environmental management; Forecasting; Literature reviews; Load; Loads (forces); Public policy & environmental management; Regression analysis; Subtropical zones; Switching; Variables; SETAR; Autoregressive; Load; Electricity; TAR; Regime switching; Non-linear; Forecasting; Threshold
• ISSN: 1750-6220; 1750-6239
• DOI: 10.1108/IJESM-02-2014-0001

Purpose
– The purpose of this study is to ascertain whether nonlinearities could be present in electricity loads observed in subtropical environments, where none or little heating is required, and whether threshold autoregressive (TAR)-type regime switching models could be advantageous in the modeling of those loads.
Design/methodology/approach
– The actual observed load of a Brazilian regional electricity distributor from January 2013 to August 2012 was modeled using a popularly employed ARMA model for reference, and smooth and non-smooth TAR transition (non-linear) models were used as non-linear regime switching models.
Findings
– Evidence of nonlinearities were found in the load series, and evidence was also found on the intrinsic resistance of this type of models to structural breaks in the data. Additionally, to reacting well to asymmetries in the data, these models avoid the use of exogenous variables. Altogether, this could prove to be a definite advantage of the use of such model alternatives.
Research limitations/implications
– However, even if the present work may have been limited by the observation frequency of the available data, it appears TAR models appear to be a viable alternative to forecasting short-term electricity loads. Nonetheless, additional research is required to achieve a higher accuracy of forecast data.
Practical implications
– If such models can be successfully used, it will be a great advantage for electricity generators, as the computational effort involved in the use of such models is not significantly larger than regular linear ones.
Originality/value
– To our knowledge, this type of research has not yet been made with subtropical/tropical electricity load data.