Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles

This paper presents an improved technique for optimal power generation using ensemble artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor computing rather than traditional serial computation to reduce bias and variance in machine learning. The load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN) with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power generation. The results of MATLAB simulations are analyzed and compared along with computational complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed to LR.