Abstract
This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI’s Energies journal. The special issue took place in 2017 and accepted a total of 13 papers from 7 different countries. Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance.
Highlights
This special issue has focused on the forecasting of time series with data mining and big data techniques, paying particular attention to energy related data
Energy was understood to be of any kind, such as electrical, solar and wind
Electricity demand forecasting has been addressed by using deep learning [1], ensemble learning [2] and the functional state space model [3]
Summary
This special issue has focused on the forecasting of time series with data mining and big data techniques, paying particular attention to energy related data. Energy was understood to be of any kind, such as electrical, solar and wind. Authors were invited to submit their original research and review articles on exploring the issues and applications of energy time series and forecasting. Topics of primary interest included, but were not limited to: (1) Energy-related time series analysis; (2) Energy-related time series model; (3) Energy-related time series forecasting; (4) Non-parametric time series approaches.
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