Recurrent Neural Networks for Short-term Load Forecasting: An Overview and Comparative Analysis

The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system.

Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures.

Dr. Filippo Maria Bianchi is a postdoctoral researcher in the Department of Physics and Technology at the Arctic University of Norway, Tromsø, Norway. Dr. Michael C. Kampffmeyer is a research fellow at the same institution. Dr. Robert Jenssen is an associate professor at the same institution. Dr.  Enrico Maiorino is a research fellow in the Channing Division of Network Medicine at Harvard Medical School, Boston, MA, USA. Dr. Antonello Rizzi is an assistant professor in the Department of Information Engineering, Electronics and Telecommunications at the Sapienza University of Rome, Italy.