Optimizing Load Forecasting Configurations of Computational Neural Networks

Abstract

Load demand forecasting is a broad branch of electric power systems engineering. In the last few decades, hundreds of methods have been suggested by many brilliant researchers around the world to improve the current forecasting tools. These tools are categorized as: deterministic, probabilistic, stochastic, and artificial intelligent (AI) algorithms. Among these approaches, artificial neural networks (ANNs) proved themselves as very competitive and highly precise methods to accurately forecast energy. ANNs are divided into two main types called: feed-forward networks and recurrent/feedback networks, where each type of them has multiple sub-types. This study tries to improve the performance of any existing type/sub-type of ANNs by optimizing its configuration through using the biogeography-based optimization (BBO) algorithm. The number of input variables, layers, neurons, and the types of activation functions and training algorithm all are optimized. The goal is to preserve the simplicity, so only very simple multi-layer feed-forward ANNs are used instead of using time-series-based feed-forward/feedback ANNs. To prove the effectiveness of hybridizing ANNs with evolutionary algorithms (EAs), numerical simulations are carried-out on some Nova Scotia's loads. The results obtained from these optimally configured ANN s are highly significant, and thus they confirm that allegation.