A Taguchi and Neural Network Based Electric Load Demand Forecaster

Abstract

Abstract:In this paper, we present Taguchi's and rolling modeling methods of artificial neural network (ANN) for very-short-term electric demand forecasting (VSTEDF) from the consumers' viewpoint. The rolling model is a metabolism technique that guarantees input data are always the most recent values. In ANN prediction, several factors that may influence the model should be well examined. Taguchi's method was employed to optimize the parameter settings for the ANN-based electric demand-value forecaster. Our experimental result shows that the optimal settings of ANN prediction model are 3 lagged load points, 0.1 for the momentum, 5 hidden neurons and 0.1 for the learning rate. The error of forecasting is as small as 3%. That is, comparison with the results of ordinary ANN and Grey prediction, the presented Taguchi-ANN-based forecaster gives more accurate prediction for VSTEDF.

Minimization of Residual Stresses in Submerged Arc Welding Process of Oil and Gas Steel Pipes by Committee Machine

“…As usual, MRO is solved in three phases include experiments design, modeling and optimization. Experiments design is arranged based on some known patterns in Design of Experiments (DOE) knowledge such as factorial design and Taguchi orthogonal arrays [5][6][7][8]. Second phase is done by means of different mathematical or statistical modeling such as multiple linear regression which uses polynomials to model [9][10][11] and Artificial Neural Networks (ANNs).…”