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Application of Artificial Intelligence for Renewable Energy Power Forecasting
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Application of Artificial Intelligence for Renewable Energy Power Forecasting

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: 5 December 2024 | Viewed by 2125

Special Issue Editors

Dr. Óscar Trull

E-Mail Website
Guest Editor
Department of Applied Statistics and Operational Research, and Quality, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: time series analysis; short-term forecasting of electricity demand; forecasting in the time domain
Special Issues, Collections and Topics in MDPI journals
Prof. J. Carlos García-Díaz

E-Mail Website
Guest Editor
Department of Applied Statistics and Operational Research, and Quality, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: time series analysis; short-term forecasting of electricity demand; forecasting in the time domain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energies is running a Special Issue on the topic of the “Application of Artificial Intelligence for Renewable Energy Power Forecasting.” A new model of electricity production based on clean and renewable sources is being implemented with increasing speed in all countries. Climate change, the high prices of raw materials such as gas and oil, and conflicts in producing countries are more than enough reasons for society to direct its gaze towards clean and renewable energy production. In addition, the possibility of having small production units, even private ones, arouses even more interest in this type of generation.

However, one of the main problems that concerns this type of generation is the difficulty of making accurate predictions about the production that will be possible. These are energy sources, for example, that depend greatly on the surrounding weather conditions, among other things.

It is at this point where artificial intelligence regains special interest due to its versatility when it comes to making predictions in time series that are difficult to model.

The objective of this Special Issue is to present new emerging methodologies based on artificial intelligence and/or hybrid models in which artificial intelligence plays a determining role. Of particular interest are new methods, characterized by high uncertainty and volatility, that can help to improve decision making in current energy markets.

For this reason, we encourage researchers to submit their contributions in this field in order to advance current scientific knowledge and develop practical and/or real applications of these technologies

Dr. Óscar Trull
Prof. J. Carlos García-Díaz
Guest Editors

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • artificial intelligence
  • forecasting
  • load
  • power
  • renewable energy

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Published Papers (2 papers)

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Research

13 pages, 3506 KiB  
Article
Adaptive Bi-Directional LSTM Short-Term Load Forecasting with Improved Attention Mechanisms
by Kun Yu
Energies 2024, 17(15), 3709; https://doi.org/10.3390/en17153709 - 27 Jul 2024
Viewed by 659
Abstract
Special load customers such as electric vehicles are emerging in modern power systems. They lead to a higher penetration of special load patterns, raising difficulty for short-term load forecasting (STLF). We propose a hierarchical STLF framework to improve load forecasting accuracy. An improved [...] Read more.
Special load customers such as electric vehicles are emerging in modern power systems. They lead to a higher penetration of special load patterns, raising difficulty for short-term load forecasting (STLF). We propose a hierarchical STLF framework to improve load forecasting accuracy. An improved adaptive K-means clustering algorithm is designed for load pattern recognition and avoiding local sub-optimal clustering centroids. We also design bi-directional long-short-term memory neural networks with an attention mechanism to filter important load information and perform load forecasting for each recognized load pattern. The numerical results on the public load dataset show that our proposed method effectively forecasts the residential load with a high accuracy. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence for Renewable Energy Power Forecasting)
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20 pages, 5362 KiB  
Article
Short-Term Photovoltaic Output Prediction Based on Decomposition and Reconstruction and XGBoost under Two Base Learners
by Weihui Xu, Zhaoke Wang, Weishu Wang, Jian Zhao, Miaojia Wang and Qinbao Wang
Energies 2024, 17(4), 906; https://doi.org/10.3390/en17040906 - 15 Feb 2024
Cited by 2 | Viewed by 925
Abstract
Photovoltaic power generation prediction constitutes a significant research area within the realm of power system artificial intelligence. Accurate prediction of future photovoltaic output is imperative for the optimal dispatchment and secure operation of the power grid. This study introduces a photovoltaic prediction model, [...] Read more.
Photovoltaic power generation prediction constitutes a significant research area within the realm of power system artificial intelligence. Accurate prediction of future photovoltaic output is imperative for the optimal dispatchment and secure operation of the power grid. This study introduces a photovoltaic prediction model, termed ICEEMDAN-Bagging-XGBoost, aimed at enhancing the accuracy of photovoltaic power generation predictions. In this paper, the original photovoltaic power data initially undergo decomposition utilizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm, with each intrinsic mode function (IMF) derived from this decomposition subsequently reconstructed into high-frequency, medium-frequency, and low-frequency components. Targeting the high-frequency and medium-frequency components of photovoltaic power, a limiting gradient boosting tree (XGBoost) is employed as the foundational learner in the Bagging parallel ensemble learning method, with the incorporation of a sparrow search algorithm (SSA) to refine the hyperparameters of XGBoost, thereby facilitating more nuanced tracking of the changes in the photovoltaic power’s high-frequency and medium-frequency components. Regarding the low-frequency components, XGBoost-Linear is utilized to enable rapid and precise prediction. In contrast with the conventional superposition reconstruction approach, this study employs XGBoost for the reconstruction of the prediction output’s high-frequency, intermediate-frequency, and low-frequency components. Ultimately, the efficacy of the proposed methodology is substantiated by the empirical operation data from a photovoltaic power station in Hebei Province, China. Relative to integrated and traditional single models, this paper’s model exhibits a markedly enhanced prediction accuracy, thereby offering greater applicational value in scenarios involving short-term photovoltaic power prediction. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence for Renewable Energy Power Forecasting)
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