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 (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 naïve 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.

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

“… 38 In this paper, the main contribution is to design a high-accuracy framework based on the STLF for real-time electrical demand estimation and then validated the real-time data for the enhancement of the proposed intelligent framework. Further studies have shown in, 39 that the real-time hourly based STLF model is effectively employed in 20 zonal areas in the USA. The intelligent-based short-term forecast approach rapidly increasing in industries as well as the electrical power market because of the high system reliability and accuracy.…”

Identification and control of a heat flow system based on the Takagi-Sugeno fuzzy model using the grey wolf optimization algorithm

“…The framework for designing Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems using GA was proposed in [12]. Paper [13] uses a GA to select the best inputs for different multiple linear regression models. A multitude of improved particle swarm optimization (PSO) algorithms are presented in variations of works [14][15][16][17].…”