This document describes the backpropagation algorithm for training multilayer artificial neural networks (ANNs). It discusses the key aspects of the backpropagation algorithm including: the initialization of weights and biases, feedforward propagation, backpropagation of error to calculate weight updates, and updating weights and biases. It provides pseudocode for the backpropagation training algorithm and discusses factors that affect learning like learning rate and momentum. It also gives an example of using backpropagation for load forecasting in power systems, showing the network architecture, training algorithm, and results.