Electric Load Forecasting Model Using a Multicolumn Deep Neural Networks

Abstract

In this article, a new approach to short-term load forecasting is proposed using a multicolumn radial basis function neural network (MCRN). The advantage of this new approach over similar models in speed and accuracy is also discussed, especially in regards to renewable generation forecasting. Because weather and seasonal effects have a direct impact not only on load demand but also on renewable energy production, it follows that as the penetration rate of renewable DG increases, the grid will become even more sensitive to weather impacts in the long term. In our approach, we use a k-d tree algorithm to split our feature-rich dataset into dense specialized subsets. These subsets are then trained in parallel as multiple artificial neural networks using a modified error correction algorithm to form the MCRN. This approach reduces the number of hidden neurons, increases the speed of convergence, and improves generalization over similar alternative forecasting methods.