Short-Term Load Forecasting 2019

Dear Colleagues,

It is well known that short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies for power system (planning, scheduling, maintenance, and control processes, among others), and this topic has been an important issue for decades. However, there is still so much to do in this field. The deployment of enabling technologies (e.g., smart meters) has made high granular data available for many customer segments and for many tasks, for instance, to make feasible load forecasting tasks at several demand aggregation levels. The first challenge is the improvement of the STLF models and their performance at new demand aggregation levels. Moreover, the increasing inclusion of renewable energies (wind and solar power) in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the near future.

Many techniques have been proposed for STLF, including traditional statistical models (such as SARIMA, ARMAX, exponential smoothing, linear and non-linear models, etc.) and artificial intelligence techniques (such as fuzzy regression, artificial neural networks, support vector regression, tree-based regression, ensemble methods, stacked methods, etc.). Furthermore, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new uncertainties sources in the power system, has given more importance to probabilistic load forecasting in recent years.  

This Special Issue is concerned with both the fundamental research on STLF methodologies and practical application research, in order to face the future challenges of a more distributed power system in the future.

All of the submitted contributions must be based on the rigorous motivation of the mentioned approaches, and demonstrate a theoretically sound framework; submissions lacking such a scientific approach are discouraged. It is reccomended that existing/presented approaches are validated using real practical applications.

Prof. Dr. Antonio Gabaldón
Prof. Dr. Dr. María Carmen Ruiz-Abellón
Prof. Dr. Luis Alfredo Fernández-Jiménez
Guest Editors

Manuscript Submission Information

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