Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The...

Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and...

Distributed control applied to combined electricity and natural gas infrastructures

The optimization of combined electricity and natural gas systems is addressed in this paper. The two networks are connected via energy hubs. Using the energy hub concept, the interactions between the different infrastructures can be analyzed. A system consisting of several interconnected hubs forms a distributed power generation structure where...