Enhanced ELITE-Load: A Novel CMPSOATT Methodology Constructing Short-Term Load Forecasting Model for Industrial Applications

This article presents a two-layer hybrid neural network framework, termed enhanced ELITE (E-ELITE), for short-term load forecasting (STLF) with high-performance forecasting capability and accuracy. The design of the E-ELITE is based on a novel three-stage methodology that is composed of Stage I: optimal structure, Stage II: highly accurate and diverse members of neural network, and Stage III: a neural network-based ensemble. We explore the capability of our proposed consensus-based mixed-integer particle swarm optimization-assisted TRUST-TECH (CMPSOATT) method in each of the three-stage methodology to achieve the following advantages: first high-quality local optimal solutions or even the global optimal solution and second computational speed; consequently, the forecasting accuracy and generalization ability of the E-ELITE is enhanced by CMPSOATT via a training procedure to design a diverse set of optimal ANN forecasting engines in the bottom layer and another training procedure to achieve higher performance results than any engines in the top layer. The resulting E-ELITE is shown to obtain a more accurate, scalable, and better generalization STLF forecasting performance with less computational effort than other powerful forecasting methods through a utility-scale dataset.