THERMAL SCIENCE

International Scientific Journal

Slobodan A. Ilić

,

Aleksandar Selakov

,

Srđan Vukmirović

PROCEDURE FOR CREATING CUSTOM MULTIPLE LINEAR REGRESSION BASED SHORT TERM LOAD FORECASTING MODELS BY USING GENETIC ALGORITHM OPTIMIZATION

ABSTRACT
This paper presents a novel procedure for short-term load forecasting in distribution management systems. The load is forecasted for feeders that can be of a primarily residential, commercial, industrial or combined type. Each feeder has various amounts of distributed energy resources installed, which accounts for multiple different load patterns. Hence, the distribution management systems cannot use a single short-term load forecasting model for all forecasts. The proposed procedure addresses the specificity of each particular feeder type, by creating customized short-term load forecasting models. It uses a genetic algorithm to select the best inputs for different multiple linear regression models. The genetic algorithm chooses variables from a dataset constructed using load and temperature measurements. The dataset is extended by adding non-linear transformations and mutual interaction effects of the measurements, as well as calendar variables. This extension enables for the modelling of non-linear influences and extracts the non-linearity to the domain of input variables. The models’ performance is assessed by the mean absolute percentage error. The proposed procedure is applied to a set of measurements collected from an US electric power utility, which operates in the city of Burbank, Cal., USA. The obtained multiple linear regression model is compared with a previously proposed naïve benchmark, and a special comparison model, developed by correlation analysis. The proposed method is extendable to suit distribution management systems with different types of electricity consumers.
KEYWORDS
short-term load forecasting, genetic algorithm, input variable selection, multiple linear regression
PAPER SUBMITTED: 2019-12-05
PAPER REVISED: 2020-01-25
PAPER ACCEPTED: 2020-02-07
PUBLISHED ONLINE: 2020-03-08
DOI REFERENCE: https://doi.org/10.2298/TSCI191205101I
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2021, VOLUME 25, ISSUE Issue 1, PAGES [679 - 690]
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Procedure for creating custom multiple linear regression based short term load forecasting models by using genetic algorithm optimization

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence