Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

Dear Colleagues,

The growing utilization of data collectors in energy systems has resulted in the collection of a very high volume of data. For instance, smart sensors are now widely used in energy production and energy consumption systems. It follows that such Big Data allow a number of opportunities and challenges for informed decision-making. 

Very powerful approaches have been developed in the context of data science and big data analytics in recent years. Such approaches deal with huge datasets, considering all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing or data visualization are now being successfully applied to energy demand forecasting. 

The aim of this Special Issue is to gather the latest advancements in energy demand forecast, and in particular with the use of advanced optimization methods and Big Data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, wind. 

We encourage researchers to share their original works in the fields of energy demand forecasting, with a particular emphasis on applications. Topics of primary interest include but are not limited to: 

1. Advanced optimization methods for energy demand forecast;
2. Big Data techniques for energy demand forecast;
3. Optimization methods and big data in energy-related time series forecasting;
4. Optimization methods and big data in nonparametric time series approaches;

Prof. Dr. Federico Divina
Prof. Dr. Francisco A. Gómez Vela
Prof. Dr. Miguel García-Torres
Guest Editor

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