This paper proposes a Taguchi-based optimization framework for short-term load forecasting (STLF) using single-layer feedforward neural networks (SLFNs). Although SLFNs are computationally efficient, their accuracy strongly depends on proper hyperparameter configuration, which is often selected through inefficient trial-and-error procedures. The proposed approach applies orthogonal arrays and signal-to-noise analysis to identify robust and reproducible SLFN settings. A MATLAB-based load forecasting interface is developed to support data preprocessing, model selection, parameter tuning, forecasting, and performance evaluation. The methodology is validated using real historical load and meteor... More >