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.
PAPER SUBMITTED: 2019-12-05
PAPER REVISED: 2020-01-25
PAPER ACCEPTED: 2020-02-07
PUBLISHED ONLINE: 2020-03-08
, VOLUME 25
, ISSUE Issue 1
, PAGES [679 - 690]
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