Electricity Demand Forecasting Using Computational Intelligence and High Performance Computing

This article presents the application of parallel computing for building different computational intelligence models applied to the forecast of the hourly electricity demand of the following day. The short-term forecast of electricity demand is a crucial problem to define the dispatch of generators. In turn, it is necessary to define demand response policies related with smart grids. Computational intelligence models have emerged as successful methods for prediction in recent years. The large amount of existing data from different sources and the great development of supercomputing allows to build models with adequate complexity to represent all the variables that improves the prediction. Parallel computing techniques are applied to obtain two artificial neural network architectures and its related parameters to forecast the total electricity demand of Uruguay for the next day. These techniques consists in train and evaluate models in parallel with different architectures and sets of parameters using grid search techniques. Furthermore each model is trained using Tensorflow with finite-grained GPU parallelism. Considering the high computing demands of the applied techniques, they are developed and executed on the high performance computing platform provided by National Supercomputing Center (Cluster-UY), Uruguay. Standard performance metrics are applied to evaluate the proposed models. The experimental evaluation of the best model reports excellent forecasting results. This model has a mean absolute percentage error of \(4.3\%\) when applied to the prediction of unseen data.