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This paper presents a novel procedure for short-term load forecasting (STLF)   in distribution management systems (DMS). 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 (DER)   installed, which accounts for multiple different load patterns. Hence, the   DMS cannot use a single STLF model for all forecasts. The proposed procedure   addresses the specificity of each particular feeder type, by creating   customized STLF models. It uses a genetic algorithm (GA) to select the best   inputs for different multiple linear regression (MLR) models. The GA chooses   variables from a dataset constructed using load and temperature   measurements. The dataset is extended by adding nonlinear transformations   and mutual interaction effects of the measurements, as well as calendar   variables. This extension enables for the modelling of nonlinear influences   and extracts the nonlinearity to the domain of input variables. The models’   performance is assessed by the mean absolute percentage error (MAPE). The   proposed procedure is applied to a set of measurements collected from a US   electric power utility, which operates in the city of Burbank, CA. The   obtained MLR model is compared with a previously proposed naive benchmark,   and a special comparison model, developed by correlation analysis. The   proposed method is extendable to suit DMS systems with different types of   electricity consumers.

Procedure for creating custom multiple linear regression based short term load forecasting models by using genetic algorithm optimization