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Hybrid Energy Forecasting Models

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 4640

Special Issue Editor


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Guest Editor
EA 4935 LaRGE, Laboratoire de Recherche en Géosciences et Énergies, Université des Antilles, 97170 Pointe-á-Pitre, France
Interests: solar energy; wind energy; stochastic methods; stochastic modeling; atmospheric wind speed; times series analysis; turbulence

Special Issue Information

Dear Colleagues,

The main aim of this Special Issue is to provide a forum for researchers covering the whole range of hybrid forecasting applications to intermittent renewable power generation.

The installed capacity for energy from solar farms, wind farms, and marine energy systems is constantly increasing in response to worldwide interest in low-emissions power sources and a desire to decrease the dependence on petroleum. The variability and unpredictability of this kind of resources over short, medium, and large time scales remains a challenging task, as its penetration of this energy into the electric grid is limited. Hence, forecasting of renewable energy power generation is playing a key role and is of real practical importance in managing the electrical network for integrating this kind of energy.

For this Special Issue, we would like to encourage original contributions regarding recent developments and ideas and review articles covering applications of hybrid forecasting in renewable power generation. The Special Issue will focus on the most important forecasting techniques applied to renewable energies management, including but not limited to the following:

  • Statistical forecasting models;
  • Multiscale decomposition method: Fourier decomposition, Wavelet decomposition, EMD, EEMD, CEMD, etc.;
  • Regime-switching models;
  • Artificial Intelligence: Fuzzy, ANN, Machine Learning, SVR, etc.;
  • Hybrid and combined models;
  • Hierarchical and probabilistic models
  • Optimization

We invite you to submit your original work to this Special Issue and look forward to receiving your outstanding research.

Prof. Dr. Rudy Calif
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wind speed
  • solar radiation
  • wind energy
  • solar energy
  • wave energy
  • time series
  • forecasting techniques
  • multiscale analysis

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Published Papers (1 paper)

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Research

20 pages, 1703 KiB  
Article
Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market
by Jiang Wu, Feng Miu and Taiyong Li
Energies 2020, 13(7), 1852; https://doi.org/10.3390/en13071852 - 10 Apr 2020
Cited by 28 | Viewed by 3998
Abstract
Crude oil is one of the strategic energies and plays an increasingly critical role effecting on the world economic development. The fluctuations of crude oil prices are caused by various extrinsic and intrinsic factors and usually demonstrate complex characteristics. Therefore, it is a [...] Read more.
Crude oil is one of the strategic energies and plays an increasingly critical role effecting on the world economic development. The fluctuations of crude oil prices are caused by various extrinsic and intrinsic factors and usually demonstrate complex characteristics. Therefore, it is a great challenge for accurately forecasting crude oil prices. In this study, a self-optimizing ensemble learning model incorporating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sine cosine algorithm (SCA), and random vector functional link (RVFL) neural network, namely ICEEMDAN-SCA-RVFL, is proposed to forecast crude oil prices. Firstly, we employ ICEEMDAN to decompose the raw series of crude oil prices into a group of relatively simple subseries. Secondly, RVFL is used to forecast the target values for each decomposed subseries individually. Due to the complex parameter settings of ICEEMDAN and RVFL, SCA is introduced to optimize the parameters for ICEEMDAN and RVFL in the above decomposition and prediction stages simultaneously. Finally, we assemble the predicted values of all individual subseries as the final predicted values of crude oil prices. Our proposed ICEEMDAN-SCA-RVFL significantly outperforms the single and ensemble benchmark models, as demonstrated by a case study conducted using the time series of West Texas Intermediate (WTI) daily crude oil spot prices. Full article
(This article belongs to the Special Issue Hybrid Energy Forecasting Models)
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