Recent Advances in Energy Time Series Forecasting

Abstract

This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI's Energies journal. The special issue took place in 2016 and accepted a total of 21 papers from twelve different countries. Electrical, solar, or wind energy forecasting were the most analyzed topics, introducing brand new methods with very sound results. Keywords: energy; time series; forecastingThis special issue has focused on the forecasting of time series, with particular emphasis on energy-related data. By energy, it was understood to mean any kind of energy, such as electrical, solar, or wind.Authors were invited to submit their original research and review articles 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. From all the submissions received, only those with very high quality scientific content and innovativeness were accepted, after rigorous peer review. A total of twenty-one papers were accepted, with the following author's geographical distribution:(1) China (9). (2) Spain (4). The submissions received can be broadly divided into the following topics. First, electricity demand forecasting has been addressed by using deep neural networks [1], cointegration techniques [2], random forests [3], imbalanced classification for outlying data [4], or non-linear autoregressive neural networks [5]. Another hot topic-that is, electricity price forecasting-has also been analyzed in this special issue by means of an empirical mode decomposition-based multiscale methodology [6] or by

Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning

“…Moreover, some researchers provide importance to classical methods providing capabilities that include problem solving, storing memory, and understanding human language [34]. In fact, problems concerning energy-related data for time series forecasting, more specifically electrical, solar, and wind energy problems, have a great impact on society [35].…”

A Novel Prediction Approach for Short-Term Renewable Energy Consumption in China Based on Improved Gaussian Process Regression

“…In aiming for obtaining a satisfactory forecasting performance, a number of approaches have been developed to predict energy consumption, for instance: time series analysis [11], the Long-range Energy Alternatives Planning System (LEAP) [12,13], the Nanoelectromechanical systems approach (NEMS) [14,15], computational intelligence technology [16] and hybrid forecasting systems [17,18]. However, although the models listed above have strong non-linear modeling ability, they cannot capture the characteristics of small-scale samples very well.…”