Abstract
Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1\% have been achieved, which is a 30\% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. In addition, in order to improve the robustness of the forecast to variations in the number of neurons and other network parameters, the author proposes a method using an exponential decay of the error weights for training the neural network. The modification consists in giving higher error weight to more recent values and lower weight to older values of the training set. By doing this, mover recent values have a higher influence on the calculation of the synaptic weights and therefore the forecast produced by the NARX network is more accurate. This method, combined with the use of Bayesian regularization for training, results in improved forecast accuracy of up to 25\% and robustness to variation in parameter selection. The New England electrical load data are used to train and validate the forecast prediction.