Short-term load forecasting of power systems by gene expression programming

Abstract

Short-term load forecasting is a popular topic in the electric power industry due to its essentiality in energy system planning and operation. Load forecasting is important in deregulated power systems since an improvement of a few percentages in the prediction accuracy will bring benefits worth of millions of dollars. In this study, a promising variant of genetic programming, namely gene expression programming (GEP), is utilized to improve the accuracy and enhance the robustness of load forecasting results. With the use of the GEP technique, accurate relationships were obtained to correlate the peak and total loads to average, maximum and lowest temperatures of day. The presented model is applied to forecast short-term load using the actual data from a North American electric utility. A multiple least squares regression analysis was performed using the same variables and same data sets to benchmark the GEP models. For more verification, a subsequent parametric study was also carried out. The observed agreement between the predicted and measured peak and total load values indicates that the proposed correlations are capable of effectively forecasting the shortterm load. The GEP-based formulas are relatively short, simple and particularly valuable for providing an analysis tool accessible to practicing engineers.

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

“…Fan and Zhu indicated that a combination of empirical mode decomposition (EMD) and GEP may perform higher accuracy than WD and GEP combination for short-term load forecasting [23]. Hosseini and Gandomi compared GEP models with multiple least squares regression (MLSR) and generalized regression neural networks (GRNN) for forecasting day ahead peak and total loads of a North American electric utility [24]. Deng et al used artificial fish swarm based hybrid GEP along with cloud computing in order to model distributed electric load forecasting in comparison with ANN, PSO-SVM, SVR, and traditional GEP on the data set of EUNITE competition [25].…”

“…Although parse tree demonstration is used in traditional GP, GEP employs a fixed length of character strings ([+, *, *, β 1 ,x 1 , β 2 , x 2 ] for the expression tree in Figure 4) for illustrating solutions to the problems, which are then visualized as parse trees [24]. The illustration of trees in GEP is named as expression tree and shown in Figure 4.…”

A new prediction model for the load capacity of castellated steel beams

“…A basic representation of the GEP algorithm is presented in Fig. 4 [26]. In GEP, the individuals are selected and copied into the next generation according to the fitness by roulette wheel sampling with elitism.…”

Component Thermodynamical Selection Based Gene Expression Programming for Function Finding

“…Because of its high performance, GEP has attracted increasing attention recently as an efficient and effective data mining approach. Moreover, it has been successfully applied to many fields, such as function finding [7][8][9], symbolic regression [10][11][12][13], parameter optimization [14], rule mining [15], classification [3,16], time series forecasting [2], prediction of flow number of asphalt mixes [17], prediction of material load [18,19], prediction of the strength of concrete [20], engineering design [21], and machine scheduling [22,23].…”