Towards estimation of electricity demand utilizing a robust multi-gene genetic programming technique

Abstract

Multi-gene genetic programming (MGGP) is a new nonlinear system modeling approach that integrates the capabilities of standard genetic programming and classical regression. This paper deals with the application of this robust technique for the prediction of annual electricity demand in Thailand. The predictor variables included in the analysis were population, gross domestic product, stock index, and total revenue from exporting industrial products. Several statistical criteria were used to verify the validity of the model. A sensitivity analysis was performed to evaluate the contributions of the input features. The correlation coefficients between the measured and predicted electricity demand values are equal to 0.999 and 0.997 for the calibration and testing data sets, respectively. In addition to its high accuracy, MGGP outperforms regression and other powerful soft computing-based techniques.

Modeling Energy Demand—A Systematic Literature Review

“…ML has the advantages of being able to handle nonlinear relations and achieve high levels of accuracy with quite low implementation effort [22]. Drawbacks lie in the black-box character [190], the tendency of overfitting and getting stuck at shallow local minima [116,191]. Countermeasures are the use of regularization procedures, the formation of model ensembles as well as feature selection and data pre-processing by decomposition.…”

“…Historic energy demand [34,37,39,40,49,[71][72][73]112,116,117,136,150,163,175,[189][190][191][192]254,363,407,427,437,446,447] Weather data [22,37,39,40,112,136,150,163,175,191,254,363,407,427,437,446,447] Calendar data [39,73,112,136,150,446,447] Demographic or economic data [34,…”

Fast Prediction of Solute Concentration Field in Rotationally Influenced Fluids Using a Parameter-Based Field Reconstruction Convolutional Neural Network

“…MGGP is an additive summation model, in which some trees can be weighted and linearly combined by multigene symbolic regression. Compared with some other machine learning algorithms, such as the GP approach, the complexity of the model is reduced because the required depth of trees is lowered, and thus a relatively compact model can be generated [65]. At the comparable level of complexity, the MGGP technique is typically more accurate, as an MGGP chromosome allows for multiple genes.…”